From 5635ac9e61e8362ced629b1ccdeac83df6a521be Mon Sep 17 00:00:00 2001 From: igerber Date: Sun, 1 Nov 2026 18:39:51 -0500 Subject: [PATCH] release: 4.0.0 - close the 4.0 cut window (item 10) Bump __version__ / pyproject / rust/Cargo.toml / llms-full.txt / CITATION.cff (version + date-released 2026-11-01) to 4.0.0, which closes the ledger's cut window (cut_window 4.0 / cut_base 3.13.0 stay as the historical record; the due gate is green at 4.0.0 with 94 removed / 43 done / 2 permanent shimmed rows M-031, M-082). CHANGELOG.md gains the compiled `## [4.0.0] - 2026-11-01` section from the 26 changelog.d fragments and the v3.13.0...v4.0.0 compare link; the compiler's CATEGORIES order now leads with Breaking Changes / Behavioral Changes (README mirror + order test extended), the pr1-conformance fragment's LWDiD pvalue/ci bullet moved from `### Deprecated` to `### Breaking Changes` (the properties are removed, not deprecated), and the cut-framing fragment no longer claims a maint/3.13 branch was cut (none was - maintainer decision 2026-10-18). Prose flipped to present/past tense: docs/user_guide.rst ("upcoming 4.0"), docs/migration-4.0.md (intro note - incl. the unwarned field breaks and the numbers-move caveat for the df_convention / auto-cluster flips; the renames intro; the 3.9 alias-diet sentence), ROADMAP.md (the shipped 4.0 program entry replaced by the 4.1 event_study() front door), DEFERRED.md (Version-gated intro; the migration-guide snippet-execution waiver's premise restated for both directions of the mixed fences), .claude/memory.md (Results Objects block is the 4.0 contract), METHODOLOGY_REVIEW.md (three "will be removed" sentences about params removed at 4.0), docs/troubleshooting.rst and REGISTRY.md (the static TWFE time= alias raises since 4.0, M-083), diagnostic_report.py (comment: reading a removed overall_* name raises AttributeError), tutorial 10_trop markdown (the twostep/joint aliases are gone), tutorial 18 (stored warning path prefix stripped), .claude/commands/bump-version.md (__init__ line number). Spec docs/v4-design.md: status CLOSED at 4.0.0; forward-reference disclaimer, companion-artifacts pointer and dated-addenda list amended; section 2 cut-window paragraph marked spent (no maint/3.13 branch); phase-5 row and section 9 items 0 and 10 marked shipped; the 4.0-cut checklist records the deprecation grep re-run (0 warning-emitting sites, 0 shim helper call sites, 0 `.. deprecated::` directives, no module __getattr__; four stale prose sentences fixed here); section 11 notes the window closed. Ledger header comments note the closure. Tutorials re-executed on the Rust backend with the worktree kernel: 02 and 09 (the item-7a p-value/CI moves and the item-7b `Std. errors: ... G=` lines; point estimates and SEs unchanged), 27 (the M-030 `post=` spelling in the practitioner text; warning path prefixes normalized), 25 (its committed confidence-set / in-time-placebo outputs were a stale capture: a probe at its own last commit, 3f79ff1c / #842, already prints the refreshed numbers on the Python backend, so no 4.0 commit moved them). 01/04/05/07/11/13 have never carried outputs and are nbmake-validated; 16_wooldridge_etwfe was refreshed at 8b; 32's Python-backend bootstrap outputs are left as captured (backend-only difference). TODO.md's tutorial re-execution row is closed. --- .claude/commands/bump-version.md | 2 +- .claude/memory.md | 22 +- .claude/scripts/changelog_compile.py | 4 +- CHANGELOG.md | 600 ++++++++++++++++++ CITATION.cff | 4 +- DEFERRED.md | 4 +- METHODOLOGY_REVIEW.md | 9 +- ROADMAP.md | 4 +- TODO.md | 1 - changelog.d/20261011-python-3-15.md | 6 - .../20261016-reviewer-eval-gpt6-prereg.md | 28 - .../20261017-reviewer-eval-gpt61sol-prereg.md | 23 - .../20261018-npm-lock-security-refresh.md | 10 - changelog.d/20261018-v4-cut-framing.md | 29 - changelog.d/20261018-v4-pr1-conformance.md | 28 - changelog.d/20261018-v4-pr2-shim-removals.md | 60 -- ...261020-v4-pr3a-aggregate-test-migration.md | 25 - ...1-v4-pr3b-aggregate-removals-mechanical.md | 55 -- .../20261021-v4-pr3c-aggregate-relay.md | 85 --- .../20261021-v4-pr4a-std-qdid-removals.md | 42 -- .../20261023-v4-pr4b-tutorials-twfe-es.md | 29 - .../20261024-v4-pr4c-i-twfe-es-decoupling.md | 26 - .../20261025-v4-pr4c-ii-b-docs-benchmarks.md | 12 - changelog.d/20261025-v4-pr4c-ii-remove-mpd.md | 57 -- .../20261026-v4-pr5a-ii-docs-readers.md | 13 - .../20261026-v4-pr5a-overall-att-readers.md | 16 - changelog.d/20261026-v4-pr5b-storage-flip.md | 8 - ...261027-v4-pr6-ddd-container-unification.md | 12 - .../20261028-v4-pr7a-df-convention-flip.md | 7 - .../20261029-v4-pr7b-m080-cluster-policy.md | 10 - ...29-v4-pr7c-m154-spillover-df-convention.md | 7 - ...261030-v4-pr8a-m093-sentinel-retirement.md | 7 - ...0261031-v4-pr8b-m093-event-study-schema.md | 7 - .../20261101-v4-pr9-m090-docs-reorg.md | 9 - ...01-v4-pr9b-shim-retirement-placebo-time.md | 52 -- changelog.d/README.md | 7 +- diff_diff/__init__.py | 2 +- diff_diff/diagnostic_report.py | 4 +- diff_diff/guides/llms-full.txt | 2 +- docs/methodology/REGISTRY.md | 2 +- docs/migration-4.0.md | 18 +- docs/troubleshooting.rst | 4 +- docs/tutorials/02_staggered_did.ipynb | 264 ++++---- docs/tutorials/09_real_world_examples.ipynb | 252 ++++---- docs/tutorials/10_trop.ipynb | 2 +- docs/tutorials/18_geo_experiments.ipynb | 2 +- .../25_synthetic_control_policy.ipynb | 126 ++-- .../27_cic_distributional_effects.ipynb | 116 ++-- docs/user_guide.rst | 4 +- docs/v4-deprecations.yaml | 5 +- docs/v4-design.md | 83 ++- pyproject.toml | 2 +- rust/Cargo.toml | 2 +- tests/test_changelog_fragments.py | 16 +- 54 files changed, 1110 insertions(+), 1116 deletions(-) delete mode 100644 changelog.d/20261011-python-3-15.md delete mode 100644 changelog.d/20261016-reviewer-eval-gpt6-prereg.md delete mode 100644 changelog.d/20261017-reviewer-eval-gpt61sol-prereg.md delete mode 100644 changelog.d/20261018-npm-lock-security-refresh.md delete mode 100644 changelog.d/20261018-v4-cut-framing.md delete mode 100644 changelog.d/20261018-v4-pr1-conformance.md delete mode 100644 changelog.d/20261018-v4-pr2-shim-removals.md delete mode 100644 changelog.d/20261020-v4-pr3a-aggregate-test-migration.md delete mode 100644 changelog.d/20261021-v4-pr3b-aggregate-removals-mechanical.md delete mode 100644 changelog.d/20261021-v4-pr3c-aggregate-relay.md delete mode 100644 changelog.d/20261021-v4-pr4a-std-qdid-removals.md delete mode 100644 changelog.d/20261023-v4-pr4b-tutorials-twfe-es.md delete mode 100644 changelog.d/20261024-v4-pr4c-i-twfe-es-decoupling.md delete mode 100644 changelog.d/20261025-v4-pr4c-ii-b-docs-benchmarks.md delete mode 100644 changelog.d/20261025-v4-pr4c-ii-remove-mpd.md delete mode 100644 changelog.d/20261026-v4-pr5a-ii-docs-readers.md delete mode 100644 changelog.d/20261026-v4-pr5a-overall-att-readers.md delete mode 100644 changelog.d/20261026-v4-pr5b-storage-flip.md delete mode 100644 changelog.d/20261027-v4-pr6-ddd-container-unification.md delete mode 100644 changelog.d/20261028-v4-pr7a-df-convention-flip.md delete mode 100644 changelog.d/20261029-v4-pr7b-m080-cluster-policy.md delete mode 100644 changelog.d/20261029-v4-pr7c-m154-spillover-df-convention.md delete mode 100644 changelog.d/20261030-v4-pr8a-m093-sentinel-retirement.md delete mode 100644 changelog.d/20261031-v4-pr8b-m093-event-study-schema.md delete mode 100644 changelog.d/20261101-v4-pr9-m090-docs-reorg.md delete mode 100644 changelog.d/20261101-v4-pr9b-shim-retirement-placebo-time.md diff --git a/.claude/commands/bump-version.md b/.claude/commands/bump-version.md index 3cd2613d4..52b1ac926 100644 --- a/.claude/commands/bump-version.md +++ b/.claude/commands/bump-version.md @@ -20,7 +20,7 @@ Files that need updating: | File | Format | Line | |------|--------|------| -| `diff_diff/__init__.py` | `__version__ = "X.Y.Z"` | ~134 | +| `diff_diff/__init__.py` | `__version__ = "X.Y.Z"` | ~332 | | `pyproject.toml` | `version = "X.Y.Z"` | ~7 | | `rust/Cargo.toml` | `version = "X.Y.Z"` | ~3 | | `CHANGELOG.md` | Section header + comparison link | Top + bottom | diff --git a/.claude/memory.md b/.claude/memory.md index 557b856ea..81d77d231 100644 --- a/.claude/memory.md +++ b/.claude/memory.md @@ -81,18 +81,16 @@ results = estimator.fit(df, outcome='y', treatment='treated', ...) print(results.summary()) ``` -### Results Objects — CURRENT (3.x) -The canonical inference quintet is `att` / `se` / `t_stat` / `p_value` / -`conf_int` (never `.pvalue` / `.ci`). Every results class exposes all five, -but native STORAGE varies in 3.x (`overall_att` on the staggered family, -`avg_att` on the MultiPeriodDiD class removed at the 4.0 cut, with canonical -names as properties). `summary()` -everywhere; `to_dict()` / `to_dataframe()` on most classes. See -`.claude/../docs/methodology/REGISTRY.md` per estimator. - -### Results Objects — 4.0 TARGET (do not write against this pre-4.0) -Target contract: `docs/v4-design.md` section 5; per-surface lifecycle: -`docs/v4-deprecations.yaml` (CI-enforced by `tests/test_v4_matrix.py`). +### Results Objects — CURRENT (4.0) +The canonical inference quintet `att` / `se` / `t_stat` / `p_value` / +`conf_int` is the NATIVE storage on every results class since 4.0 (the 3.x +`overall_*` names, `EventStudyResults.period_effects` and LWDiD's `.pvalue` / +`.ci` were removed at 4.0 - `AttributeError`; 3.x pickles still load via +`__setstate__`). `summary()` everywhere; `to_dict()` / `to_dataframe()` on +most classes; `to_dataframe(level="event_study")` emits the unified +`EVENT_STUDY_SCHEMA`. Contract: `docs/v4-design.md` section 5; per-surface +lifecycle: `docs/v4-deprecations.yaml` (CI-enforced by +`tests/test_v4_matrix.py`). See `docs/methodology/REGISTRY.md` per estimator. ### Column Naming — CURRENT (4.0) - `unit` unit id everywhere (the 3.x `unit_col` on HAD and `group` on dCDH diff --git a/.claude/scripts/changelog_compile.py b/.claude/scripts/changelog_compile.py index 76040f871..db35889fb 100644 --- a/.claude/scripts/changelog_compile.py +++ b/.claude/scripts/changelog_compile.py @@ -36,6 +36,8 @@ # order. changelog.d/README.md mirrors this list; the mirror is pinned by # tests/test_changelog_fragments.py (README<->compiler parity test). CATEGORIES = ( + "Breaking Changes", + "Behavioral Changes", "Added", "Changed", "Deprecated", @@ -45,8 +47,6 @@ "Performance", "Documentation", "Testing", - "Breaking Changes", - "Behavioral Changes", "Internal", ) diff --git a/CHANGELOG.md b/CHANGELOG.md index b682e756d..75eff45c4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,605 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 +## [4.0.0] - 2026-11-01 + +### Breaking Changes +- **`LWDiDResults.pvalue` and `LWDiDResults.ci` are removed** (ledger rows `M-151`, `M-152`): the + 3.10-born alias properties for `p_value` / `conf_int` were first shimmed with a `FutureWarning` + and then removed outright at 4.0 (item 9b of the cut). Read `.p_value` / `.conf_int`. +- **`LWDiDResults.to_dataframe()` is reshaped onto the shared results schema** (ledger row + `M-153`): each `level` returns `aggregate(level).to_dataframe()` - columns `level, label, + target, att, se, t_stat, p_value, conf_int_lower, conf_int_upper, n, weight, df` (the + event-study schema for `level="event_study"`) - plus LWDiD's `rolling`, `estimation_method` + and `vcov_type` columns. The default `"simple"` is ONE headline row; the former per-cohort + rows are `level="group"` (without the old trailing `"Overall"` row). The `term` / `cohort` + column is now `label`, `n_treated` is the schema's `n`, `n_obs` and `n_control` are no longer + in the frame (both stay in `to_dict()`), and `ci_lower` / `ci_upper` are + `conf_int_lower` / `conf_int_upper`. `to_csv()` follows and takes the same `level`. +- **`BusinessReport.to_dict()` schema `2.0` -> `3.0`**: the StackedDiD `assumption` block no longer + carries the legacy `clean_control` key (read `control_group`). `DiagnosticReport`'s schema stays + `2.0` at this step. (Intermediate: later bullets in this release move the final 4.0 versions to + `BUSINESS_REPORT_SCHEMA_VERSION` 4.0 and `DIAGNOSTIC_REPORT_SCHEMA_VERSION` 3.0.) +- Passing `aggregate=` or `balance_e=` to `CallawaySantAnna.fit`, `EfficientDiD.fit`, + `StackedDiD.fit`, `ChaisemartinDHaultfoeuille.fit` or `HeterogeneousAdoptionDiD.fit` (or + `aggregate=` to `did_had_pretest_workflow`) now raises Python's native + `TypeError: ... unexpected keyword argument`. `TripleDifference`'s staggered-mode + `aggregate=`/`balance_e=` are untouched at this step (`M-140`/`M-141`; they are REMOVED later in + this release by the DDD container unification bullet below). +- **Positional slots after the removed parameters shift left.** `CallawaySantAnna`: + `survey_design` 9 -> 7; `EfficientDiD`: `survey_design` 9 -> 7, `store_eif` 10 -> 8; + `StackedDiD` (`aggregate` sat before them): `population`, `survey_design`, `covariates` + 7/8/9 -> 6/7/8; `ChaisemartinDHaultfoeuille`: `L_max`, `covariates`, `trends_linear`, + `trends_nonparam`, `honest_did`, `heterogeneity`, `design2`, `survey_design` each move one + slot left (7..14 -> 6..13). `HeterogeneousAdoptionDiD.fit` shifts nothing (everything after + the removed slot is keyword-only; a seventh positional now raises). Pass these by keyword. +- `CallawaySantAnnaResults.to_dataframe(level="event_study"|"group")` and the + `EfficientDiDResults` twins raise on every fresh fit (the legacy fields are always `None`); + use `results.aggregate(...).to_dataframe()`. (Intermediate: the `M-093` schema bullet below + supersedes this - since item 8b `to_dataframe(level="event_study")` recomputes the surface on + these classes instead of raising; `level="group"` still raises with the post-fit pointer.) +- Passing `aggregate=` or `balance_e=` to `ImputationDiD.fit`, `TwoStageDiD.fit` or + `ContinuousDiD.fit` now raises Python's native `TypeError: ... unexpected keyword argument`. +- **Positional slots after the removed parameters shift left.** `ImputationDiD` and + `TwoStageDiD`: `survey_design` 9 -> 7; `ContinuousDiD`: `survey_design`, `covariates` + 8/9 -> 7/8. Pass these by keyword. +- `aggregate('event_study', balance_e=k)` on a BOOTSTRAPPED `ImputationDiD` / `TwoStageDiD` + fit raises `NotImplementedError` (the relayed surface was computed unrestricted; re-fit with + `n_bootstrap=0` for a balanced window). Analytic fits recompute with `balance_e` as before. +- A bootstrapped `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` fit WITH bootstrap inference + but WITHOUT a stored surface - a 3.9-3.13 bootstrapped plain-fit pickle, or a fit whose + fail-soft materialization dropped it - raises `ValueError` from `aggregate('event_study')` / + `aggregate('group')` (previously `NotImplementedError` from the bootstrap gate); the message + names the refit. A 3.9-3.13 bootstrapped pickle fitted with `balance_e=k` stores a restricted + surface and the relay returns it as stored. +- **`TripleDifference.fit(time=)` in the 2x2x2 design now raises `ValueError`** naming `post=` + (`M-085`). Through 3.x `time=` was the deprecated alias for the 0/1 post indicator and emitted + a `FutureWarning` (`M-031`); since 4.0 `time` means the staggered calendar column only. + Supplying both `post=` and `time=` keeps raising the "pass only post=" error. +- Keyword mapping for callers of the removed class: `eligibility=` is `partition=` on + `TripleDifference`; the staggered fit parameters are keyword-only; `cluster=` raises in + staggered mode (use `n_bootstrap > 0` for unit-level clustered inference). Users of + `balance[did]` stay on 3.x through that extra's `diff-diff<4` pin, where the old name still + resolves. +- **`TwoWayFixedEffects.fit(time=)` in static mode now raises `ValueError`** naming `post=` + (`M-083`). Through 3.x `time=` was the deprecated alias for the 0/1 post indicator and + emitted a `FutureWarning` (`M-082`); since 4.0 `time` means the event-study calendar column + only. Supplying both `post=` and `time=` keeps raising the "pass only post=" error. +- **`EventStudyResults.period_effects` is removed** (`M-016`; the compatibility view + this item first shipped was retired at 4.0 by item 9b - no release carried it). Through + the pre-release window, on a calendar surface it returned the former calendar-keyed dict of + per-period records (`period, effect, se, t_stat, p_value, conf_int, is_significant, + significance_stars`, reference period omitted; `is_significant` is evaluated at the fit's + `alpha`, where the removed record hard-coded 0.05; duplicate labels raise). Relative-time + `aggregate("event_study")` surfaces do not carry it (`AttributeError`, no warning). The + successor is `to_dataframe()` and the columnar arrays. +- The 3.x "default reference_period has changed" `FutureWarning` is no longer emitted + (`M-007`): the event-study mode defaults to the last pre-period silently. +- `simulate_power` and friends read the flat `att`/`se`/`p_value`/`conf_int` names from + unregistered custom estimators' results and raise `TypeError` naming `_EstimatorProfile` + otherwise (the `avg_*` fallback left with the removed container; pass `result_extractor=` + for other shapes). + +- **4.0 cut, storage flip of the headline quintet (`M-050`..`M-058`).** `CallawaySantAnnaResults` (and `DMLDiDResults`), `SunAbrahamResults`, `ImputationDiDResults`, `TwoStageDiDResults`, `StackedDiDResults`, `EfficientDiDResults`, `WooldridgeDiDResults`, `ChaisemartinDHaultfoeuilleResults` and `ContinuousDiDResults` now store the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` as their native dataclass fields (renamed in slot, so the positional constructor layout is unchanged) and take those names as constructor keywords (`overall_att=` raises `TypeError`). The 3.x `overall_att` / `overall_se` / `overall_t_stat` / `overall_p_value` / `overall_conf_int` names - and on `ContinuousDiDResults` all nine legacy spellings, the `overall_att_*` fields and the former silent `overall_se`-style aliases - are removed at 4.0 (item 9b retired the `FutureWarning` property shims this item first shipped - reading one raises `AttributeError`); they are absent from `dataclasses.fields()`, `asdict()` and `to_dict()`. 3.x pickles load unchanged (each container's `__setstate__` rewrites the legacy keys on load). The bootstrap draw containers (`bootstrap_results.overall_att_se` and siblings) are unchanged; `StaggeredTripleDiffResults` kept its `overall_*` storage at this step and is removed by the DDD container unification bullet below. +- **Report schemas.** `DiagnosticReport.to_dict()` reports `headline_metric.name` and `target_parameter.headline_attribute` as `"att"` for the nine containers above (`"overall_att"` under 3.x); `DIAGNOSTIC_REPORT_SCHEMA_VERSION` 2.0 -> 3.0. `BusinessReport.to_dict()` reports `target_parameter.headline_attribute` likewise; `BUSINESS_REPORT_SCHEMA_VERSION` 3.0 -> 4.0 (its `headline` block carries no name key). `StaggeredTripleDiffResults` kept `headline_attribute: "overall_att"` at this step (removed by the DDD unification below). + +- **4.0 cut, DDD container unification (`M-014`, `M-140`, `M-141`).** `TripleDifference.fit()` returns ONE `TripleDifferenceResults` for both designs: `results.design` is `"2x2x2"` or `"staggered"`, the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` are native in both, the 2x2x2 positional layout is unchanged, and the staggered structure (`group_time_effects`, `groups`, `time_periods`, unit counts, `control_group`, `base_period`, `anticipation`, `bootstrap_results`, ...) is appended with defaults (`group_time_effects` is `{}` in the 2x2x2 design). The 3.x `StaggeredTripleDiffResults` class, its module, its `overall_*` fields and `to_dict()` keys, and its fit-time `overall_att_es` / `overall_se_es` / `overall_t_stat_es` / `overall_p_value_es` / `overall_conf_int_es` fields are REMOVED (3.x staggered pickles cannot load; 3.x 2x2x2 pickles load unchanged). The fit-time `aggregate=` / `balance_e=` keywords of `TripleDifference.fit()` are REMOVED (`TypeError`): aggregation is post-fit - `results.aggregate('event_study')` (`EventStudyResults`, `balance_e=`), `results.aggregate('group')` (`AggregationResult`) and `results.aggregate('simple')` (a one-row relay of the stored headline, both designs); bootstrapped fits REPLAY the fit-time multiplier bootstrap so sup-t bands and percentile inference match a 3.x fit-time aggregation; the 2x2x2 design fails the staggered levels closed. The paper's Eq. 4.14 overall is `results.aggregate('simple', weights='event_time')` (accepts `balance_e=`; both 3.x warnings preserved). `summary()` no longer prints the event-study / cohort / Eq. 4.14 blocks; `to_dataframe('event_study' | 'group')` raise with the post-fit pointer; `to_dataframe(level=None)` defaults to `'group_time'` in the staggered design and `'overall'` in 2x2x2 (whose one-row frame gains `design`, `pscore_trim`, `alpha`, `df_inference`). New field `df_inference` records the 2x2x2 engine's t df. `plot_group_effects` and its siblings raise `ValueError` on a 2x2x2 fit (no cells). +- **Report schemas.** For staggered DDD fits `DiagnosticReport.to_dict()['estimator']` and `BusinessReport.to_dict()['estimator']['class_name']` read `TripleDifferenceResults`, and `target_parameter.headline_attribute` reads `"att"` (the last `"overall_att"`). Both schemas gain an additive key - DR a top-level `design`, BR `estimator.design` - `"2x2x2"` / `"staggered"` for `TripleDifferenceResults`, `None` otherwise. No version bump: `DIAGNOSTIC_REPORT_SCHEMA_VERSION` 3.0 and `BUSINESS_REPORT_SCHEMA_VERSION` 4.0 had not shipped (folded into the PR 5b bump). `power.py`'s staggered-boundary guard no longer keys on `aggregate` / `balance_e` (an `estimator_kwargs` carrying them surfaces as `fit()`'s `TypeError`). + +- **4.0 cut, `df_convention` default flip (`M-004`, `M-005`, `M-006`, `M-128`, `M-129`, `M-130`, `M-131`).** The analytical tail-df convention for CLUSTERED fits defaults to `"cluster"` (the Stata/fixest `t(G - 1)` reference) instead of `"residual"` (the fitted residual df `t(n - K_full)`) on `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends leads); `LPDiD` already defaulted to `"cluster"` at 3.9. What moves: p-values and confidence intervals on clustered analytical fits - `TwoWayFixedEffects` (static, and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS and `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` moves on the `pretrends=True` lead rows; `DifferenceInDifferences` / `LinearRegression` move only with an explicit `cluster=` / `cluster_ids=`; the analytical lanes inside bootstrapped fits (`WooldridgeDiD` per-cell inference and post-fit `aggregate()`, `ImputationDiD` bootstrapped leads) move too, as do the fake-timing / fake-group placebo diagnostics and the power simulators that relay those p-values. What does not: point estimates, standard errors and t-statistics; survey-design fits (survey df keeps precedence); `vcov_type="hc2_bm"` (Bell-McCaffrey DOF keep precedence); bootstrap percentile p-values / CIs; unclustered and Conley fits; the BJS overall/post inference of `ImputationDiD`; `WooldridgeDiD` logit/poisson arms; permutation and leave-one-out diagnostics. `df_convention="residual"` restores the 3.x inference bit-for-bit; on the surfaces where the knob is a documented no-op (non-`pretrends` `ImputationDiD`, `WooldridgeDiD` logit/poisson) the inert-config `UserWarning` now fires for an explicit `"residual"` rather than for an explicit `"cluster"`. `SyntheticDiD` inherits the flipped attribute from `DifferenceInDifferences` without reading it (no behaviour change). + +- `SpilloverDiD` auto-clusters at `unit` when `cluster=` is omitted ([M-080], item 7b of the 4.0 cut), mirroring `TwoWayFixedEffects`' carve-outs (explicit `cluster=` wins; the auto-cluster is dropped on the Conley path and never injected as a survey PSU). Its default standard errors, p-values and CIs move; `cluster=False` reproduces the 3.x observation-level HC1 numbers. Results report `cluster_name="unit"` / `n_clusters`. Inference stayed on the residual df at this step; the `df_convention` knob (next bullet, [M-154]) moves it to `t(G - 1)` in the same release. +- `cluster=False` is the uniform explicit off-switch ([M-080]): the plain heteroskedasticity-robust sandwich with residual-df t inference on `TwoWayFixedEffects` (static + event-study; one-way CR2 Bell-McCaffrey under `hc2_bm`), `SunAbraham`, `WooldridgeDiD` (OLS within-transform lane with the absorbed-FE full-K rescale; logit/poisson plain QMLE sandwich) and `LPDiD` (per-horizon HC1; `if_robust` unclustered IF variance on the regression-adjustment path; implicit per-observation PSUs under a survey design); an accepted bit-identical no-op on the unit-level-by-construction estimators (`CallawaySantAnna` incl. its RC auto-PSU path, `EfficientDiD` incl. `hausman_pretest`, `DMLDiD`, `ChaisemartinDHaultfoeuille`, staggered `TripleDifference`, `LWDiD` incl. the sensitivity helpers, `HeterogeneousAdoptionDiD`, `ImputationDiD`, `TwoStageDiD`) and on the 2x2 `DifferenceInDifferences` / 2x2x2 `TripleDifference` surfaces; still rejected on `StackedDiD`. Rejected with `inference="wild_bootstrap"` (DiD/TWFE), `WooldridgeDiD(n_bootstrap > 0)` and `vcov_type="hc2_bm"` on `SunAbraham`/`WooldridgeDiD` (cluster-only Bell-McCaffrey DOF; tracked in TODO.md). +- `cluster` accepts `str | None | False` only ([M-155], `diff_diff.utils.validate_cluster`): `True` and non-`str` column labels raise one shared `ValueError` at construction, through `set_params` and at `fit()` on every estimator exposing the parameter (DMLDiD's existing rule generalized; integer-labelled cluster columns must be renamed). +- Cluster metadata: `DifferenceInDifferences` / `TwoWayFixedEffects` report `n_clusters` from the regression's effective cluster count on non-survey analytical fits (previously `None` unless the wild bootstrap ran); `SunAbrahamResults` gains an appended `n_clusters` field and reports `cluster_name` on clustered analytical fits (previously `None` off the Conley path), and its `summary()` renders the variance-family label on analytical non-survey fits; `TwoWayFixedEffects` under a survey design with no explicit `cluster=` reports `cluster_name=None` (the auto-cluster is never injected as a PSU). + +- `SpilloverDiD` gains the three-value `df_convention` knob with the 4.0 default `"cluster"` ([M-154], item 7c of the 4.0 cut): p-values and confidence intervals on its clustered lanes - the unit auto-cluster and an explicit `cluster=` - now use the Stata/fixest `t(G - 1)` reference like every other auto-clustered default, with `G` the effective cluster count reported as `n_clusters`. Point estimates, standard errors and t-statistics never move. `df_convention="residual"` restores the 3.x residual-df inference bit-for-bit; `"normal"` is deliberate z. `"cluster"` falls back to the residual df on `cluster=False` and on every `vcov_type="conley"` fit, an explicit product-kernel `cluster=` included (`"normal"` still selects z there); a `survey_design` df keeps precedence under every value; a saturated stage-2 sample stays all-NaN under every value. `SpilloverDiDResults` reports `df_convention` and `inference_df` (also in `to_dict()`). + +- Event-study reference rows are marked explicitly ([M-093], item 8a of the 4.0 cut): every native `event_study_effects` dict that materializes its normalization row (`StackedDiD`, `ImputationDiD`, `TwoStageDiD`, `SpilloverDiD`, the `CallawaySantAnna`/`DMLDiD` universal-base aggregation carrier) now carries `"is_reference": True` on that row and NO count key there - the 3.x `n_obs == 0` / `n_groups == 0` sentinel is retired, and every remaining zero count is a genuine count (a non-estimable horizon). `SpilloverDiD`'s event-study `att_dynamic` / `spillover_effects` frames gain a boolean `is_reference` column with `n_obs = NaN` on the reference rows (the column is float; `to_dict()` records follow). The native `to_dataframe(level="event_study")` frames of `StackedDiD` / bootstrapped `ImputationDiD` / `TwoStageDiD` rendered the reference row's `n_obs` as NaN (float column) at this step; the 8b schema rewire (next bullet) replaces those frames. `plot_event_study`, `plot_honest_event_study`, HonestDiD, PreTrendsPower and `DiagnosticReport` locate the reference by the key on their raw-dict routes - dispatch, admission, rendering and exception types are unchanged. `TwoStageDiD.summary()` now prints its genuine all-filtered horizons as NaN rows instead of a mislabeled `[ref: h]` line. 3.x pickles are migrated on load. `SunAbraham` (omitted row, `reference_period`) and `EfficientDiD` (PT-Post anchor, an estimated cell named by `reference_period`) are unchanged. + +- Every results class's `to_dataframe(level="event_study")` now emits the unified `EVENT_STUDY_SCHEMA` ([M-093], item 8b of the 4.0 cut): `event_time, att, se, t_stat, p_value, conf_int_lower, conf_int_upper, cband_lower, cband_upper, n, df, is_reference, estimand` - the frame `results.aggregate("event_study").to_dataframe()` returns (`CallawaySantAnna`/`DMLDiD`, `EfficientDiD`, `ImputationDiD`, `TwoStageDiD`, `StackedDiD`, `ContinuousDiD`, `TripleDifference` staggered, `ChaisemartinDHaultfoeuille`, HAD event-study results, `DurationDiD`, `LWDiD`) or the builder frame (`SunAbraham`, `LPDiD`, `WooldridgeDiD`, `SpilloverDiD`). The 3.x bespoke frames are gone: `relative_period` -> `event_time`; `effect` / `att_glob` / LPDiD's `coefficient` -> `att`; `conf_int_lo`/`hi`, `conf_low`/`high`, HAD's `conf_int_low`/`high` -> `conf_int_lower`/`conf_int_upper`; `n_obs` -> `n` (NaN on the `is_reference` row); dCDH's `horizon` -> `event_time` on the paper's `l1_first_switch` convention, its per-horizon `estimand` labels (`DID^pl_l` / `ref` / `DID_l`) are not carried (`estimand` is `"att"`) and its reference row is marked `is_reference` instead of `n_obs 0`. `WooldridgeDiD` and `LWDiD` append their fit-configuration columns after the schema (`cohort_trends`, `aggregation_weights`; `rolling`, `estimation_method`, `vcov_type`), and `WooldridgeDiD.to_dataframe(level="event_study")` (its default level) now RAISES before `aggregate("event_study", weights=)` instead of returning an empty frame. `CallawaySantAnna`, `EfficientDiD`, `TripleDifference` and analytical `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` no longer raise "not stored on 4.0 fits" - the level recomputes the surface from the retained kit (bootstrapped `CallawaySantAnna` / `EfficientDiD` / `TripleDifference` fits replay the fit-time bootstrap on every call; an `ImputationDiD` replicate-weight survey fit with `pretrends=True` raises `NotImplementedError` as `aggregate()` does); a kit-less pre-3.9 pickle, or a bootstrapped pickle predating the replay state, that still carries a stored dict now raises where it returned a frame. `level="group"` on those three classes still raises with the post-fit pointer. New levels: `SpilloverDiDResults.to_dataframe(level="simple" | "event_study")` (default `"simple"`, the former one-row frame; aggregate-mode fits raise on `"event_study"`), `HeterogeneousAdoptionDiDEventStudyResults.to_dataframe(level="event_study")`, `DurationDiDResults.to_dataframe(level="event_study")`. An `LPDiD` fit whose stored event-study frame is empty raises instead of returning the empty frame. + +- **4.0 cut, item 9b: the compatibility shims scheduled for a 5.0 removal are removed at 4.0** (maintainer + decision - no 4.x debt; none of these `FutureWarning` shims ever reached a release, so the released + 3.x names break without a warned release - `docs/migration-4.0.md` is the notice). The 3.x + `overall_att` / `overall_se` / `overall_t_stat` / `overall_p_value` / `overall_conf_int` names on + `CallawaySantAnnaResults` (and `DMLDiDResults`), `SunAbrahamResults`, `ImputationDiDResults`, + `TwoStageDiDResults`, `StackedDiDResults`, `EfficientDiDResults`, `WooldridgeDiDResults`, + `ChaisemartinDHaultfoeuilleResults` and - all nine spellings, `overall_att_*` included - + `ContinuousDiDResults` raise `AttributeError`; read the canonical `att` / `se` / `t_stat` / + `p_value` / `conf_int` (`M-050`..`M-058`). 3.x pickles still load (the `__setstate__` key migration + stays). `EventStudyResults.period_effects` is gone; read `to_dataframe()` or the columnar arrays + (`M-016`). `LWDiDResults.pvalue` / `.ci` are gone; read `.p_value` / `.conf_int` (`M-151`, `M-152`). +- **`SyntheticControl(inner_max_iter=, inner_min_decrease=)` are removed** (`M-148`, `M-149`; the + `DeprecationWarning` shipped in 3.13.0): passing either is a `TypeError`, and the positional + parameters after them (`standardize`, `alpha`, `seed`, `v_cv_t0`) move two slots left - pass + them by keyword. +- **`run_placebo_test` / `run_all_placebo_tests`: `time` is the calendar column only and + `post_periods` is required for every `test_type`** (`M-156`, `M-157`; spec section 8 rule 1). The + 0/1 post dummy the permutation and leave-one-out tests need is derived as + `data[time].isin(post_periods)`; a missing `time` column, missing/empty `post_periods`, missing + values in `time`, `post_periods` values absent from the column, or a pre-existing `_placebo_post` + column raise `ValueError` (on the suite wrapper before any test runs). Calls that passed the 0/1 + indicator as `time=` with `post_periods=[1]` return identical numbers; calls that omitted + `post_periods` must add it. + +### Added +- **Python 3.15 support**: `requires-python` now allows 3.15 (`<3.16`) and + the 3.15 classifier is declared; release wheels are built for CPython 3.15 + on Linux (x86_64, aarch64), macOS arm64 and Windows, and CI runs the + Rust-backend matrix and the pure-Python fallback on 3.15. The Rust + extension builds against PyO3 0.30, which supports 3.15.0 final. +- **`LWDiDResults.summary(alpha=None)` / `print_summary(alpha=None)`** join the family-wide + summary-alpha guard (ledger row `M-146`): a value other than the fit-time `alpha` raises + `ValueError` instead of being ignored; the methods previously took no `alpha` at all. +- **`LWDiDResults.to_dataframe(level=...)` / `to_csv(path, level=...)`** gain the uniform + `level` selector (`"simple"` default, `"group"`, `"event_study"`) - see Breaking Changes. +- **`DurationDiDResults.aggregate("simple")`**: a verbatim relay of the stored headline + quintet (`n = n_units`, `df` NaN - inference is bootstrap-only), joining the spec section 6 + `simple` contract every aggregation adopter honours. +- **`RDDensityTestResult.to_dict()`**: JSON-serializable scalars with native casts (NaN + inference passes through), the `*_conventional` block `None` unless `report_all=True`, and + the bandwidth-selection table nested under `bandwidths` only when it was estimated. + +### Changed +- **`WooldridgeDiDResults.summary(alpha=None)`** renders the headline (simple) row only; the + group / calendar / event-study tables it used to render on request - and their `[pre]`/`[antic]` + relative-period labels - are no longer part of `summary()`. Use `aggregate(...)` / + `to_dataframe(level=...)`; `anticipation` is still reported on the results object. + `to_dataframe(level=)` now rejects an unknown level with `ValueError` instead of returning an + empty frame. +- `practitioner_next_steps` no longer recognises duck-typed results that carry only the legacy + `estimator` attribute; `ChangesInChangesResults.method` is the routing field. +- Runtime messages, the four `llms*.txt` guides, the API docs, the migration guide and the + benchmarks no longer steer users to the fit-time route on these five estimators; + `CallawaySantAnna`'s class docstring, `plot_event_study`, `honest_did` / `pretrends` and the + results-class "not stored" messages point at `results.aggregate('event_study')`. +- **Bootstrapped `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` fits relay their event-study + (and, on Imputation/TwoStage, group) surface.** The deprecated keyword was the only route to + bootstrap event-study inference there (no per-draw state is retained for a replay), so a fit + with `n_bootstrap > 0` now ALWAYS computes those surfaces at fit - exactly what + `fit(aggregate="all")` computed, `balance_e=None` - stores them in the legacy + `event_study_effects` / `group_effects` fields, and `aggregate('event_study')` / + `aggregate('group')` RELAYS them (kit-snapshot alpha / anticipation / survey metadata; NaN + `df` column; `vcov` `None`) instead of raising `NotImplementedError`. Analytic fits are + unchanged (legacy fields `None`, kit recompute on demand). Storage rule: the legacy fields + hold a surface IFF the fit carries bootstrap inference. On Imputation/TwoStage a bootstrapped + fit that ends analytical (zero valid draws, total bootstrap failure) clears them and + `aggregate()` recomputes analytically. ContinuousDiD's bootstrapped kit is scalars-only, so + no analytical fallback exists there: a bootstrapped fit with no post-treatment cells never + runs its bootstrap (overall ATT already NaN with a `UserWarning`), stores no surface, and + `aggregate('event_study')` raises the absent-surface `ValueError`. + - Materialization is FAIL-SOFT with a one-shot overall-only retry: an exception while + building the surfaces, or a bootstrap attempt that raises / returns `None` with surfaces + present, leaves them `None`, emits one `UserWarning` ("... could not be materialized ...") + and re-runs the bootstrap overall-only (a seeded fit reproduces the first attempt's weight + draws), so a plain bootstrapped fit's headline quintet and its success/abort outcome are + unchanged by the removal. ContinuousDiD bootstrap failures still propagate as before. + - Consequences on bootstrapped plain fits: the aggregators' own `UserWarning`s (ImputationDiD + Prop-5 unidentified horizons, empty horizons, LOO single-unit auxiliaries; TwoStageDiD + Stage-1/Stage-2 singular or rank-deficient fallbacks) and the per-target bootstrap-stats + `RuntimeWarning`s now fire where a plain fit never emitted them; `summary()` prints the + event-study/group tables and `to_dataframe(level="event_study"|"group")` returns frames + instead of raising; `DiagnosticReport` / `BusinessReport` run heterogeneity (Imputation / + TwoStage on the relayed group dict, ContinuousDiD on the event study) and parallel trends + (Imputation / TwoStage) on the relayed surface instead of skipping with + `NotImplementedError`; `plot_event_study(results)` plots them natively. Measured cost (199 + draws, 300 units x 12 periods): Imputation 0.01s -> 0.08s, TwoStage 0.01s -> 0.04s, + Continuous unchanged at 0.05s; all three default to `n_bootstrap=0`. +- `ImputationDiD`'s inert-`df_convention` warning predicate is now exact: the per-lead inference + is reachable iff `pretrends=True` and the design is not replicate-weighted (`M-127`); the + unreachable pretrends+replicate fit-time gate is deleted (the combination is still rejected + post-fit by `aggregate('event_study')`). +- Runtime messages (`plot_event_study`, the results-class "not stored" hints, + `_reporting_helpers`, `DiagnosticReport` docstrings), `practitioner_next_steps` advice for + bootstrapped ImputationDiD / TwoStageDiD / ContinuousDiD fits, the `llms*.txt` guides, the + ContinuousDiD API page and tutorial, the migration guide and the performance docs describe + the relay instead of steering users to the removed fit-time route. + +### Removed +- **The 3.9 rename shims are gone (ledger rows flipped to `removed`).** Old keyword spellings + now raise `TypeError`: `DifferenceInDifferences.fit(time=)` -> `post=` (`M-030`), + `permutation_test(time=)` / `leave_one_out_test(time=)` -> `post=` (`M-137`, `M-138`), + `WooldridgeDiD.fit(cohort=)` -> `first_treat=` (`M-032`), `ChaisemartinDHaultfoeuille.fit(group=, + controls=)` -> `unit=`, `covariates=` (`M-033`, `M-034`), `twowayfeweights(group=)` -> `unit=` + (`M-097`), the `*_col` spellings on `HeterogeneousAdoptionDiD.fit`, `RegressionDiscontinuity.fit`, + `RDPlot.fit`, `joint_pretrends_test`, `joint_homogeneity_test`, `did_had_pretest_workflow` + (`M-035`..`M-042`, `M-088`, `M-089`, `M-098`..`M-112`) and `trim_weights(weight_col=)` -> + `weights=` (`M-113`), `StackedDiD(clean_control=)` -> `control_group=` (`M-043`), + `WooldridgeDiDResults.to_dataframe(aggregation=)` -> `level=` (`M-044`) and + `summary(aggregation)` (`M-087`; `summary(alpha=None)` is now the positional signature), + `ContinuousDiD(covariates=)` -> `fit(covariates=)` (`M-084`). The `"event"` value spelling is + rejected with `ValueError` on `WooldridgeDiDResults.aggregate(type=)`, `.to_dataframe(level=)` and + `LPDiDResults.to_dataframe(level=)` - use `"event_study"` (`M-086`, `M-150`, `M-136`). +- **`robust=` removed** from `DifferenceInDifferences` (and so `TwoWayFixedEffects`, + `MultiPeriodDiD`), `TripleDifference`, `HeterogeneousAdoptionDiD` and `LinearRegression` + (`M-045`..`M-047`, `M-115`); `vcov_type=` is the single selector. The legacy-CR1 remap that + turned `robust=False` + clustering into `hc1` went with it. Positional constructor slots after + the removed parameter shift left by one (DiD slot 1, LinearRegression/TripleDifference slot 2, + HeterogeneousAdoptionDiD slot 6); `SyntheticDiD`'s inert `lambda_reg`/`zeta` (`M-001`, `M-002`, + constructor slots 7-8) are removed too, shifting `vcov_type` and the `conley_*` parameters two + slots left. Pass these by keyword. +- **Results-field aliases removed**: `SyntheticDiDResults.placebo_effects` (`M-003`; use + `variance_effects`), `RegressionDiscontinuityResults.treatment_col` (`M-094`; `takeup`), + `StackedDiDResults.clean_control` and `StackedDiD.clean_control` (`M-095`, `M-043`; + `control_group`), `ChaisemartinDHaultfoeuilleResults.groups` (`M-114`; `units`), + `ChangesInChangesResults.estimator` (`M-143`; `method`). Their serialized dual keys are gone from + `to_dict()` (`treatment_col`, `clean_control`, `estimator`, Wooldridge's + `aggregation_weights["event"]`); 3.x pickles still load (`__setstate__` key migrations kept). +- **The eight module-level wrapper functions are removed** (`M-070`..`M-077`): `imputation_did`, + `two_stage_did`, `stacked_did`, `trop`, `synthetic_control`, `triple_difference`, + `bacon_decompose`, `chaisemartin_dhaultfoeuille`. Call the estimator classes. `diff_diff.trop`, + `diff_diff.synthetic_control`, `diff_diff.stacked_did` and `diff_diff.chaisemartin_dhaultfoeuille` + now name the submodules (as `diff_diff.bacon` always did). +- **The dieted aliases are removed** (`M-132`..`M-134`): `CDiD` -> `ContinuousDiD`, `Stacked` -> + `StackedDiD`, `Gardner` -> `TwoStageDiD`; the module `__getattr__` warning shim (`M-135`) is gone. +- **The fit-time `aggregate=` / `balance_e=` keywords are gone on the estimators whose post-fit + route already existed (ledger rows flipped to `removed`).** `fit(aggregate=)` is removed from + `CallawaySantAnna` (`M-020`), `EfficientDiD` (`M-023`), `StackedDiD` (`M-024`), + `ChaisemartinDHaultfoeuille` (`M-026`) and `HeterogeneousAdoptionDiD` (`M-027`), + `fit(balance_e=)` from `CallawaySantAnna` (`M-117`) and `EfficientDiD` (`M-120`), and + `aggregate=` from `did_had_pretest_workflow` (`M-139`; the `HADPretestReport.aggregate` + output field survives). The successor is post-fit `results.aggregate(type, balance_e=)`; HAD + selects its mode from the panel shape. With the keywords go StackedDiD's fit-time + `group`/`all`/unknown-value `ValueError`s (`aggregate('group')` already fails closed in the + mixin) and the private `_VALID_AGGREGATES` sets in `had.py` / `had_pretests.py` + (`_infer_aggregate_mode` is the sole selector). `ImputationDiD`, `TwoStageDiD` and + `ContinuousDiD` lost their keywords in the same release once the bootstrapped-fit relay + landed (the next bullet but one). +- **CallawaySantAnna and EfficientDiD compute no event-study/group surface at fit.** Their + legacy `event_study_effects` / `group_effects` fields - and CS's `cband_crit_value`, + `event_study_vcov`, `event_study_vcov_index`, `event_study_df` - are `None` after EVERY fit; + the post-fit replay (`aggregate('event_study'|'group', balance_e=)`) is the only bootstrap + route and is unchanged. +- **The last fit-time `aggregate=` / `balance_e=` keywords are gone (ledger rows flipped to + `removed`).** `fit(aggregate=)` is removed from `ImputationDiD` (`M-021`), `TwoStageDiD` + (`M-022`) and `ContinuousDiD` (`M-025`), `fit(balance_e=)` from `ImputationDiD` (`M-118`) + and `TwoStageDiD` (`M-119`). With PR 3b's removals on CallawaySantAnna / EfficientDiD / + StackedDiD / dCDH / HAD this completes the aggregate-postfit group: every adopter aggregates + through post-fit `results.aggregate(type, balance_e=)` only; `TripleDifference`'s + staggered-mode keywords (`M-140`/`M-141`) were the last fit-time `aggregate=` at this step + and are removed in the same release by the DDD container unification (Breaking Changes + above). With the keywords go ContinuousDiD's + no-underscore `"eventstudy"` spelling and its fit-time value validation, the dead analytic + fit-time event-study inference arm in `continuous_did.py`, and ImputationDiD's unreachable + pretrends+replicate fit-time gate. +- **The `StaggeredTripleDifference` class and its `SDDD` alias are gone** (`M-013`, `M-064`; 4.0 + cut, PR 4a). `from diff_diff import StaggeredTripleDifference` and `SDDD` raise `ImportError`, + and the `diff_diff.staggered_triple_diff` module no longer exists. The staggered DDD estimator + is `TripleDifference().fit(..., unit=, time=, first_treat=, partition=)` - the same engine + (`diff_diff/_staggered_triple_diff_engine.py`), pinned against the committed oracle in + `tests/test_v4_merge_ddd.py`. R's compact `control_group` spellings + (`"notyettreated"`/`"nevertreated"`) died with the class; the merged surface takes + `"not_yet_treated"`/`"never_treated"`. +- **The `QDiD` class and the `QDiDResults` alias are gone** (`M-015`, `M-061`). The quantile-DiD + comparison estimator is `ChangesInChanges(method="qdid")` (same engine, identical numbers); + the results container is `ChangesInChangesResults` for both methods. +- The two `pyproject.toml` pytest `filterwarnings` entries that silenced the removed classes' + construction `FutureWarning`s. +- **The `MultiPeriodDiD` class, its `MultiPeriodDiDResults` container, the `PeriodEffect` + record and the `EventStudy` alias are gone** (`M-010`, `M-011`, `M-012`, `M-060`; 4.0 cut, + PR 4c-ii). `from diff_diff import MultiPeriodDiD` (and the other three names) raises + `ImportError`. The estimator is `TwoWayFixedEffects().fit(..., event_study=True, spec=, + post_periods=, reference_period=)`, which returns the unified `EventStudyResults` surface + natively: `spec="pooled"` is the removed class's design (bit-exact without `unit=`; with + `unit=` the pooled spec auto-clusters at unit), `spec="within"` is its `absorb=["unit"]` + design; `post_periods=` is required (the midpoint default died with the class) and the + post-period average `avg_att` has no successor field. Pinned field for field against the + pre-refactor capture `tests/fixtures/v4_twfe_event_study_oracles.json`. +- The `pyproject.toml` pytest `filterwarnings` entry that silenced the removed class's + construction `FutureWarning`, the `MultiPeriodDiD` power-simulation profile + (`simulate_power` has no event-study profile yet; TODO row), and the `MultiPeriodDiDResults` + branches of DiagnosticReport / BusinessReport / `practitioner_next_steps` (both reports still + reject `EventStudyResults` by design; the admission TODO row records where the logic lived). + +### Fixed + +- **`DiagnosticReport` kept every event-study horizon on fractional calendars.** The parallel-trends and heterogeneity collectors keyed the derived `aggregate('event_study')` surface (and compared raw `event_study_effects` keys) through `int(k)`, so on a half-step calendar (`e = t - g` in `-1.5, -1.0, -0.5, ...`) `-1.5` merged into `-1` and `-0.5` into the treatment horizon `0`: pre-periods silently left the PT family and a violated horizon could read as a clean verdict. Integer-valued horizons keep their `int` keys; fractional horizons stay `float`, compare at their real value against the anticipation-aware boundary, and align with the covariance index for the joint Wald. Affected every post-fit event-study producer routed through the report (CallawaySantAnna included), not only the staggered DDD route this release enables. + +- `run_all_placebo_tests` and `run_placebo_test(test_type="permutation" | "leave_one_out")` no + longer fail on a multi-period calendar column: through 3.x the wrappers forwarded `time` itself as + the 0/1 post dummy, so `DifferenceInDifferences.fit` rejected it and the suite silently reported + the two tests as error dicts (tutorial 04's placebo cell printed `ERROR` lines). The dummy is now + derived from `post_periods`. + +### Security +- **CI smoke-test lockfile refresh**: bumped the transitive `undici` (7.29.0 -> 7.30.1) + and `source-map-js` (1.2.1 -> 1.2.2) pins in `.github/scripts/package-lock.json`, + clearing all seven open Dependabot alerts. CI-only (docs search-excerpt smoke test); + nothing in the published wheel is affected. + +### Documentation +- **4.0-cut framing** (the first PR of the 4.0 cut, at v3.13.0): `docs/v4-design.md` + re-synced with the library as shipped since 3.9 - the estimator roster now counts + LWDiD/DMLDiD/DurationDiD (27 -> 24 at 4.0, 23 estimators + Bacon in the diagnostic + family), the diagnostic family names the TWFE weights containers and + `DurationDiDPretestResults`, and section 9 carries the ten-PR cut sequence. `M-080` + (auto-cluster policy) re-scoped to the measured roster: static `TwoWayFixedEffects` + already auto-clusters at unit, `SpilloverDiD` is the one estimator whose default SE + moves at 4.0, `cluster=False` becomes the uniform explicit off-switch, and the + by-construction estimators keep no auto-cluster metadata by decision (new "Default + clustering roster" section in `docs/methodology/variance-conventions.md`; REGISTRY + Notes on SpilloverDiD, StackedDiD, ImputationDiD, TwoStageDiD, DMLDiD, DurationDiD). + `M-008` (`DIFF_DIFF_SOLVE_OLS_FASTPATH` default) decided: evaluated, kept off. New + ledger row `M-150` gives `WooldridgeDiDResults.to_dataframe(level="event")` its own + locator (twin of `M-136`). The migration guide's auto-cluster section, appendix and + orientation table follow the ledger (111 appendix rows). +- **Tutorials 02/04/05/07/23 are rebuilt on `TwoWayFixedEffects().fit(..., event_study=True)`** + (`spec="within"`: unit + period fixed effects, unit-clustered SEs) ahead of the + MultiPeriodDiD removal (`M-010`, 4.0 cut PR 4c). Point estimates match the former + MultiPeriodDiD fits on these balanced panels; the SEs change because the within spec + clusters by unit. Tutorial 23's naive headline is the equal-weight average of the two + post-period coefficients computed in the cell (ATT unchanged at -4.29; the SE is now the + unit-clustered one), and tutorial 02's period-effects loop reads `to_dataframe()`. +- The static `TwoWayFixedEffects().fit(..., time=...)` calls in tutorials 01/02/09 (the + `M-082` alias, misused there as the post dummy) are replaced: 01 passes `post="post"`; + 02 and 09 report `TwoWayFixedEffects().decompose(...).twfe_estimate`, the unit + period + fixed-effects coefficient their Goodman-Bacon sections decompose (the old cells printed + `treated x period` artefacts: 0.6309 and 0.0001). Tutorial 02 cell 36's covariate draw is + seeded. Tutorials 02 and 09 are re-executed on the Rust backend, which produced the + committed outputs, so the seeded bootstrap cells are unchanged. +- Tutorials 08 and 16 describe `StaggeredTripleDifference` as the removed class (4.0 cut, + PR 4a). `diff_diff/guides/llms-full.txt`'s event-study snippet calls `print(es.summary())` + (`EventStudyResults` has no `print_summary`). + +- `BaconDecomposition` is re-homed into the diagnostics family ([M-090], item 9 of the 4.0 cut): it leaves the estimator catalogues - README `## Estimators`, `llms.txt` `## Estimators`, the `llms-full.txt` Estimators section and its alias line, the API-reference Estimators autosummary and module toctree, and the docs homepage Supported Estimators table - for their Diagnostics groupings. `RDPlot` moves from the API-reference Visualization autosummary into Diagnostics; `RDPlot`, `RDDensityTest` and the TWFE weight diagnostics leave the `llms-full.txt` Estimators section. Docs-only: the import path, the `Bacon` alias, the results class and every number are unchanged. +- The docs homepage gains a "Diagnostics & Sensitivity" table (BaconDecomposition, RDPlot, RDDensityTest, HonestDiD, PreTrendsPower, PowerAnalysis, DiagnosticReport, the parallel-trends / placebo / TWFE-weight functions and the HAD pretest workflow) next to the estimator table; the API-reference Estimators and Diagnostics intros describe the split. +- `docs/methodology/REGISTRY.md` archive framing for the classes removed at 4.0: the MultiPeriodDiD and StaggeredTripleDifference sections and the QDiD class-spelling subsection open with an archive Note, the table of contents marks them *(archived)* and states the archived-sections convention, the cross-reference tables name `ChangesInChanges(method="qdid")` instead of a standalone `QDiD` estimator, and the "20-estimator count" wording is gone. The migration guide gains the [M-090] bullet. + +### Testing +- `tests/test_t23_spillover_tva_drift.py`'s `naive_fit` fixture mirrors the notebook's TWFE + event-study headline cell, runs the fit with warnings escalated to errors, and pins the + post-period-average identity and the unit-clustered vcov-block SE alongside the existing + -4.29 endpoint. +- The MultiPeriodDiD-fixture suites (honest_did, pretrends, methodology pretrends/honest, + visualization, Conley, survey, replicate weights, vcov_type, df_convention, the estimators + event-study behaviour pins, the event-study surface roster, the DiagnosticReport / + BusinessReport generic fixtures) now run on `TwoWayFixedEffects().fit(..., event_study=True)` + (or a static TWFE fit for the report classes); MultiPeriodDiD's own tests, the MPD-only + designs (multi-`absorb`, `fixed_effects=`, wild bootstrap, container-field assertions, the + MPD-branch consumer tests) and the MPD-vs-TWFE parity oracles are unchanged until PR 4c-ii. + New `tests/test_twfe_event_study_decoupling.py`, shared helpers + `tests/helpers/twfe_es_oracle_lanes.py`, `event_study_average.py` and + `event_study_surface_builders.py`; the variance-conventions inventory gains the two TWFE + event-study rows; a TODO row tracks the one-way HC2-BM absorbed-FE R-parity lane. + +### Internal +- **Codex reviewer gpt-6 campaign pre-registration (PR-1 of 2)**: `tools/reviewer-eval` + pre-registers the `gpt-5.6-sol` -> `gpt-6-astra` evaluation (`DECISION_RULE.md`: + symmetric regression gate over every ground-truth bug, FP gate, downgrade rule, + verifier-confirmed leaks void a cell and contest the gate, stop conditions; the + 2026-07 rule is archived under `campaigns/`) and splits the grader-facing text into + `GRADING_RUBRIC.md` (marker-based `LEAK?` flag whose list renders from + `reviewer_audit.LEAK_RULES`). New engines: `reviewer_verdict.py` (gates, precedence, + binding to the sealed artifacts, protocol guards, PROVISIONAL/DEFINITIVE) and + `reviewer_audit.py` (post-unblinding session-log audit, informational and + over-matching by design). `run_eval.py` gains `audit` and `verdict`, runs every + codex call under an isolated CI-faithful `CODEX_HOME`, records seven-key git + provenance per artifact and in the manifest, seals artifacts at run time + (`artifacts_sha`), binds the blinded bundle (`bundle_id`, `run_ids_sha`, + rubric/prompt sha) and refuses post-run stages on a dirty or moved tree; `run` + holds an exclusive per-subdir lock for the whole attempt, records the OK cells of + aborted attempts too and refuses a rerun that would re-dispatch any cell an + earlier attempt recorded OK; review worktree leaves are salted opaque hashes. Harness CLI re-pinned to + codex-cli 0.162.1 (arm `E` = gpt-6-astra @ xhigh added; CI `codex-version` pinned + to the same, lockstep-tested with `DEFAULT_MODEL`, the plan-review pin, the + control arm and the prose pins). +- **`openai_review.py`**: `call_codex` parses codex's stderr session events into + `session_commands` (+ start/exec/other-event counts, truncation flag) on every + exit path and prints a one-line summary; `--timeout` now also caps the codex + backend's wall clock when passed explicitly (previously ignored there), `0` + means no timeout on both backends and negatives are rejected; `PRICING` + corrected for gpt-5.6-* and extended with gpt-6-*; gpt-6* counts as a + reasoning family; blinding scrubs gpt-6/astra and Unicode-dash spellings. +- **Codex reviewer swap gpt-5.6-sol -> gpt-6.1-sol (reviewer-eval 2026-10 campaigns, PR-2)**: + two blinded, pre-registered 44-review campaigns on the frozen `tools/reviewer-eval` + harness. Campaign 1 (B = gpt-5.6-sol vs E = gpt-6-astra, both @ xhigh, k=2): mechanical + DEFINITIVE **NO-GO** on one regression (`s4-cs-unbalanced-nan-outcome`), accepted by the + user; no astra swap (`campaigns/2026-10-gpt6-VERDICT.md`). Campaign 2 (B vs F = + gpt-6.1-sol @ xhigh, k=2, fresh control sample, same rule re-issued for F): + mechanical DEFINITIVE **PARITY** with zero rule-defined regressions, zero downgrades, + 0 vs 0 false positives, two favourable catch-status changes against one + UNSTABLE-to-MISSED judgment flag, ~15% lower latency and half the input price; the + user accepted PARITY as GO + (`campaigns/2026-10-gpt61sol-VERDICT.md`). Both verdicts ship with their redacted + grading records (`campaigns/2026-10-gpt6*-grading/`). Production pins move in lockstep + to `gpt-6.1-sol`: the CI workflow `model`, `openai_review.DEFAULT_MODEL` and the + plan-review `CODEX_MODEL` (carried over from the code-review campaign, not re-run + through plan-review-eval; TODO.md row), with the plan-review `CODEX_TIMEOUT_S` raised + 2400 -> 3600 to equal the harness ceiling. Harness: arm `F` registered with the contrast + `(B, F)`, campaign 1's rule archived as `campaigns/2026-10-gpt6-DECISION_RULE.md`, + `run --configs` defaults to the last registered shape, `PRICING` gains `gpt-6.1-sol`, + arm F takes `role=control` (B retained as previous production), the candidate-exposure + gate exempts the control arm so bare `smoke` keeps working after a swap, and the + DEFERRED `gpt-6-sol` cost-fallback row is retired (codex-cli 0.162.1's ChatGPT-account + login accepts the gpt-6 Sol tier; campaign 2 was that evaluation). +- **Dependabot npm coverage**: added an `npm` ecosystem entry for `/.github/scripts` + so the smoke-test toolchain gets weekly version-update PRs (grouped minor/patch) + instead of drifting into advisories between manual lockfile refreshes. +- **Ledger cut window** (`docs/v4-deprecations.yaml` header `cut_window: "4.0"`, + `cut_base: "3.13.0"`; spec section 11): rows scheduled at 4.0 may flip terminal + ahead of the version bump so the removals land over many PRs while `__version__` + stayed 3.13.0; `tests/test_v4_matrix.py` relaxes only the early-removal guard for the + window version, refuses any other version bump while early-terminal rows exist, and + keeps due enforcement unchanged. The id snapshot and `ROW_COUNT_FLOOR` now include + `M-148`/`M-149` (shipped by #838 without extending them) and `M-150` (132 rows). + `M-091`'s roster records the four later-born `Diagnostic` containers; `M-050`'s + `code_refs` record that `diff_diff/_deprecation.py` survives 4.0 as pickle-migration + infrastructure; `M-045`/`M-115` name the legacy-CR1 remap branches their removal + deletes. No `maint/3.13` branch was cut (maintainer decision 2026-10-18); 3.13.x + patches, if ever needed, come from the `v3.13.0` tag. +- `tests/test_v4_matrix.py` removed-status probes: a function row that resolves to a submodule + counts as absent; a param row absent from the explicit signature counts as removed despite + `**kwargs`. Naming-guard allowlist extended for the kept body-local aliases, `__setstate__` keys + and shared vocabulary. The `diff_diff._deprecation` helpers survived this PR for the remaining + M-031/M-082 and PR-3 shims (item 9b later reduced the module to its pickle-migration and + required-arg helpers). +- **4.0 cut, PR 3a - tests-first migration to the post-fit aggregation containers.** The + test and benchmark suites now read event-study / group surfaces from + `results.aggregate("event_study")` / `results.aggregate("group")` (the container API that + has existed since 3.9) instead of the deprecated fit-time `fit(aggregate=...)` keyword and + the legacy `event_study_effects` / `group_effects` fields, so the keyword removals in PRs 3b + and 3c land against already-migrated suites. No library source changes and no ledger row + flips in this PR; the fit-time keywords still work and still warn. Bootstrapped + ImputationDiD / TwoStageDiD / ContinuousDiD sites stay on the fit-time route until PR 3c + adds the post-fit relay for them. +- **Committed per-backend aggregation oracles.** `tests/test_aggregate_contract.py` no longer + uses the fit-time route as its numerical reference: every analytic surface is pinned at + `rtol=1e-12` (confidence-interval bounds at `1e-9`: scipy's `t.ppf` moved by up to 3.4e-11 + relative between 1.13 and 1.14+) and every bootstrapped CS / EfficientDiD replay at the + replay-parity tolerances against `tests/fixtures/v4_aggregate_oracles.json`, captured on the unmodified + tree at `8724fecd` by `tests/_capture_v4_aggregate_oracles.py` under both weight backends + (see `tests/fixtures/README.md`). New `tests/helpers/legacy_surface.py` (`es_dict`, + `group_dict`, `legacy_standin`) gives consumer tests a faithful stand-in for the 3.x + fit-time surface where their subject is the native (legacy-pickle) route. +- **Benchmarks** (`benchmarks/python/benchmark_callaway.py`, `benchmark_imputation.py`, the + `speed_review` scripts) call `aggregate()` post-fit inside the timed region and emit the + same JSON row shape the R refresh joins on. +- `docs/v4-design.md` section 9 records the three-way split of cut-sequence item 3 (3a + tests-first, 3b mechanical removals, 3c the Imputation/TwoStage/ContinuousDiD removals + + relay). +- `tests/test_aggregate_contract.py`: the `Test*Shim` classes, fit-time fixtures and + inertness comparisons for the five estimators are retired in favour of `Test*Removal` + pins (TypeError, signature, positional-slot binding); the committed oracle fixture is the + numerical reference. `tests/_capture_v4_aggregate_oracles.py` is marked historical (it + drives the removed route; runnable only at the recorded `captured_at_head`). Naming-guard + allowlist entries for the `llms.txt` / `llms-full.txt` canonical post-fit `aggregate` + vocabulary. Ledger row `M-093` records that CS/EfficientDiD populate the legacy fields on + no fit. `warn_deprecated_kwarg` loses its last library caller and is retained (spec section + 8 rule 11) with a direct unit pin. +- `tests/test_aggregate_contract.py`: the remaining `Test*Shim` classes, fit-time fixtures and + fail-closed bootstrap pins are retired for `Test*Removal` pins (TypeError, signature, + positional-slot binding) and relay pins - the relayed surfaces equal the stored legacy dicts + row-for-row and match the per-backend bootstrap oracles - plus failure-injection pins for the + fail-soft materialization and the storage rule. The estimator suites, `DiagnosticReport`, + `event_study_surface` (bootstrapped Imputation / TwoStage / Continuous enrolled as genuine + native producers) and practitioner tests take their relay pins; the module-level + FutureWarning filters for the fit-time route are gone. Naming-guard allowlist entries for the + guides' post-fit `aggregate` vocabulary are finalized. Ledger row `M-093` gains the fit-side + table of which producers populate the legacy fields; `M-127` records the exact predicate. + `docs/v4-design.md` section 6 carries the relay amendment and the HYBRIDS paragraph; section 9 + marks sub-item 3c shipped. +- The three StaggeredTripleDifference suites (`tests/test_staggered_triple_diff.py`, + `tests/test_methodology_staggered_triple_diff.py`, `tests/test_survey_staggered_ddd.py`) are + ported to `TripleDifference`'s staggered mode (keyword fits; the R golden's compact + `control_group` values are mapped), so the engine keeps its behavior, methodology and survey + coverage. `tests/test_v4_merge_ddd.py` / `tests/test_v4_merge_cic.py` drop their old-vs-new + parity lanes for `Test*Removal` pins; the two capture scripts are marked historical and + `make_2x2` lives in the CiC merge test. +- Ledger: `M-013`/`M-015` -> `removed`, `M-061`/`M-064`/`M-085` -> `done`; `M-031` keeps + `shimmed` with `warning: null`; `M-080` drops the deleted module from its `code_refs`. + Spec: cut-sequence item 4 split into 4a/4b/4c, the cross-row rule gains its alias clause. + `docs/doc-deps.yaml` re-primes the `staggered_triple_diff` group on the results module. +- `tests/test_v4_matrix.py` no longer requires `StaggeredTripleDifference`/`QDiD` worked-example + owners in the migration guide; the two autosummary stubs are deleted; REGISTRY keeps the + `## StaggeredTripleDifference` and `### QDiD` sections as the engine's methodology record + (archive framing lands with `M-090`). +- `docs/v4-design.md` cut-sequence item 4b marked shipped; item 4c lists the two remaining + markdown-only MultiPeriodDiD mentions (06 cell 34, 16 cell 40). `docs/doc-deps.yaml` maps + the TWFE module to tutorials 01/02/04/05/07/09/23. No ledger row flips. +- **The TWFE event-study surface is built directly from the shared core's payload** + (4.0 cut, PR 4c-i; ledger rows `M-010` / `M-011`): `DifferenceInDifferences._fit_event_study_core` + returns a private `_EventStudyCorePayload`, `TwoWayFixedEffects.fit(..., event_study=True)` + builds its `EventStudyResults` from it via `results_base._from_event_study_payload`, and only + `MultiPeriodDiD.fit` still wraps the payload into `MultiPeriodDiDResults`. `_from_mpd` and the + payload route share one calendar-surface builder, so they cannot drift; a committed oracle + captured on the pre-refactor tree (`tests/fixtures/v4_twfe_event_study_oracles.json`, capture + script `tests/_capture_v4_merge_mpd_oracles.py`) pins every field of the TWFE surface and of + its MultiPeriodDiD twin across eleven variance lanes. No user-visible behavior changes. +- Spec item 4c is split into 4c-i (this PR) and 4c-ii (the removals); the 4c-ii bullet records + the `spec="within"` benchmark-arm relabel and the `M-016` view's record type (user decisions + 2026-10-24). Ledger `M-010` / `M-011` notes updated; no status flips. +- PR 4c-ii-b (docs/benchmarks half of the MultiPeriodDiD removal): REGISTRY / REPORTING / + survey-theory / METHODOLOGY_REVIEW and the remaining user-doc prose stop describing the + removed class as live; TODO rows 33/35/38/95/99 and three DEFERRED rows that governed the + class are reworded or retired. The oracle fixture `tests/fixtures/v4_twfe_event_study_oracles. + json` drops its `mpd_twin` records and the lane metadata loses its MPD twin fields (the TWFE + records are byte-identical); the capture script documents the per-lane capture SHAs. +- Benchmarks: the live event-study arm is the new `benchmarks/python/benchmark_twfe_event_study.py` + (`spec="within"`, `cluster="unit"`; parity against R `fixest::feols(... | unit)` verified + locally - ATT to 2e-12, SE to 0.0%, per-period effects correlation 1.000); + `benchmark_multiperiod.py` and `benchmarks/refresh_2026_07/` stay frozen because that harness + runs inside pinned 3.5.3/3.7.0 venvs that predate `event_study=True`. +- `DifferenceInDifferences._fit_event_study_core` serves only the TWFE event-study mode + (`post_periods` required by the core; `include_treatment_main` / `cluster_override` + keyword-required); the private payload is pruned to the fields the surface builder reads. + The clustered `hc2_bm` contrast batch keeps the former post-period-average column as the + batch-relative Bell-McCaffrey DOF guard's anchor so per-period NaN masks stay bit-identical + (REGISTRY Note; TODO row for the guard redesign). `build_event_study_surface` opens with an + `isinstance(EventStudyResults)` passthrough. +- Ledger: `M-010`/`M-011`/`M-012` -> `removed`, `M-060`/`M-007`/`M-083` -> `done`, `M-016` + -> `shimmed` on `diff_diff:EventStudyResults.period_effects`, `M-082` keeps `shimmed` with + `warning: null`. Spec item 4c-ii carries the SHIPPED note incl. the three recorded + deviations (anchor column, new live benchmark script, DR/BR branch deletion). +- Tests: the MPD-only tests are deleted or re-fixtured onto `cs_fit` / the TWFE surface + (`tests/test_v4_merge_mpd.py` carries the removal, `M-016` and `M-007` pins and the anchor + pin); the oracle fixture gains two clustered `hc2_bm` lanes captured at `d45d5316` before + the core pruning (its `mpd_twin` records are pruned in PR 4c-ii-b); the clustered + per-period CR2 DOF R/clubSandwich pin and the event-study covariate-collision guard are + migrated to TWFE; variance-conventions `twfe_es_*` rows re-pinned (`tail_df` 5) and the table + regenerated. The methodology/benchmark prose half and the oracle fixture's + `mpd_twin` prune follow in PR 4c-ii-b. +- 4.0 cut, PR 5a-ii (the documentation half of cut-sequence item 5's reader migration, + split from PR 5a because the combined diff exceeded the AI review input budget): every + documented read of the 3.x `overall_att` / `overall_se` / `overall_t_stat` / + `overall_p_value` / `overall_conf_int` names (and ContinuousDiD's `overall_att_*` + spellings) on the nine `M-050`..`M-058` containers - RTD pages, the bundled + `llms*.txt` guides, tutorial code cells, the tutorial drift tests that mirror them + (tutorial 32's code-cell hash pins re-pinned), benchmark scripts and the carousel + generator - now reads the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` + quintet. No user-visible behaviour change (the canonical names already resolve); + committed notebook outputs are unchanged except tutorial 18's echoed guidance string. + Bootstrap draw containers (`bootstrap_results.overall_att_se`) and R / golden-JSON + interface keys are untouched. +- 4.0 cut, PR 5a (the reader half of cut-sequence item 5, the `M-050`..`M-058` storage + flip): every in-repo reader of the 3.x `overall_att` / `overall_se` / `overall_t_stat` / + `overall_p_value` / `overall_conf_int` field names (and ContinuousDiD's `overall_att_*` + spellings) on the nine flipping containers in SOURCE and TESTS - consumers, the + containers' own `summary()` / `to_dict()` / `__repr__` / aggregation bodies, and every + test suite - now reads the canonical `att` / `se` / + `t_stat` / `p_value` / `conf_int` quintet. No user-visible change: the canonical names + already resolve as properties, numbers are bit-identical and nothing warns yet; the + storage flip itself, the `FutureWarning` shims and the report-schema changes follow in + PR 5b; the documentation / guides / tutorial / benchmark half of the reader migration + is PR 5a-ii (split off because the combined diff exceeded the AI review input budget). `power.py`'s `_extract_staggered` (a silent `getattr` fallback chain over the old + names) is deleted - the staggered power profiles use `_extract_simple`; the + `practitioner_next_steps` `overall_att` fallback is gone. Bootstrap draw containers + (`CSBootstrapResults.overall_att_se` and siblings) and `StaggeredTripleDiffResults` + (unified at cut item 6) are untouched. + +- Ledger rows `M-050`..`M-058` flip to `shimmed` with `tests/test_v4_storage_flip.py` as their `test_ref`; `diff_diff._deprecation` gains `LEGACY_OVERALL_QUINTET` and `migrate_renamed_state_keys` (the pickle-migration infrastructure); `DMLDiDResults` re-declares the five shims so the warning names the subclass (naming-guard allowlist reason reworded); `_extract_scalar_headline`'s legacy alias row is deleted; `tests/test_result_aliases.py` pins the post-flip direction (SDDD kept in the 3.x direction); the API autosummary stubs are regenerated; the bootstrap draw containers' payload names are recorded as a REGISTRY `Note` (user decision 2026-10-26); spec item 5 carries its SHIPPED note. + +- Ledger rows `M-014`, `M-140`, `M-141` flip to `removed` with `tests/test_v4_unify_ddd.py` as their `test_ref`; `diff_diff/staggered_triple_diff_results.py` is renamed to `diff_diff/triple_diff_results.py` (hosting the unified container, re-exported from `triple_diff.py`); the staggered engine attaches the CallawaySantAnna aggregation kit (`staggered.py`'s `_build_aggregation_kit` on the eligibility-zeroed precomputed copy, plus the `ddd_df_survey_original` bookkeeping key for the Eq. 4.14 df rule); report consumers dispatch on the design through `_reporting_helpers.results_design` / `is_staggered_ddd` (DR's `_DESIGN_APPLICABILITY` / `_DESIGN_PT_METHOD`); the practitioner handler gains a staggered branch; `docs/api/_autosummary` regenerated; the spec's section 4.2 / section 6 / item-6 SHIPPED notes and the REGISTRY `## TripleDifference` post-fit Note record the mechanics and the `weights='event_time'` decision. + +- Ledger rows `M-004`..`M-006` and `M-128`..`M-131` flip to `done` with `tests/test_v4_inference_defaults.py` as their `test_ref`; the spec's section 7 / item 7 SHIPPED notes and item 10 now record the deferred bulk re-execution of the nine tutorials whose clustered-default outputs moved (24 re-executed here if its outputs moved); the REGISTRY TwoWayFixedEffects clustered-CR1 inference-df note is relabelled a plain Note (the default now matches fixest) and the SunAbraham / ImputationDiD / StackedDiD / WooldridgeDiD / LPDiD tail-df notes read the 4.0 default; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` and the per-estimator knob suites pin the G-1 default against explicit `"residual"` baselines (every default-vs-`"cluster"` comparison became default-vs-`"residual"` so none passes vacuously); `test_ci_matches_r_twfe` tightens to machine precision; the variance-conventions audit matrix keeps its residual-df rows as explicit `"residual"` arms (the K_full accounting pins) and gains eight G-1 `_default_df` twins (three in the fast set), with `docs/methodology/variance-conventions.md` regenerated; the committed TWFE event-study oracle lanes `pooled_cluster_unit` / `within_cluster_unit` and the ImputationDiD `pretrends_all` contract lanes name the `"residual"` convention they were captured under (metadata-only, no oracle number touched); the `DEFERRED.md` Version-gated (v4) row is retired. + +- Ledger: M-080 and the new M-155 (shared cluster contract) -> `done`; new M-154 (SpilloverDiD `df_convention`, item 7c) -> `planned`; pinned by `tests/test_v4_cluster_policy.py`. Tutorial 23 section 6 shows the four-row SE table (CR1 at unit, HC1 via `cluster=False`, Conley lag 0 / lag 1) and was re-executed; `tests/test_t23_spillover_tva_drift.py` pins the new default and the table's source. + +- Ledger: M-154 -> `done`, pinned by `tests/test_v4_spillover_df_convention.py`; the df ladder is the pure helper `spillover._resolve_inference_df` (survey df -> saturation -> `utils.resolve_tail_df`). The variance-conventions matrix gains the `spillover_cluster_unit` / `spillover_cluster_unit_default_df` rows; the REGISTRY `df_convention` knob roster, `docs/methodology/variance-conventions.md`, the migration guide, `llms-full.txt` and `docs/api/spillover.rst` describe the knob; tutorial 23 re-validated (it prints no p-values or CIs). + +- `results_base._from_relative_dict` reads only the `is_reference` key and the named `reference_period` (the zero-count heuristic is gone; a keyed row with a finite nonzero effect raises); `_deprecation.migrate_event_study_reference_sentinel` is the single place the retired sentinel is still recognized, run by the carriers' `__setstate__` (`SpilloverDiDResults` gained one; aggregate-mode frames untouched). Ledger: M-093 gains `test_ref` (`tests/test_v4_sentinel_retirement.py`) and stays `planned` until 8b (the `to_dataframe(level="event_study")` schema rewire on every results class, incl. the dCDH native frame's `estimand 'ref'` row). REGISTRY (SpilloverDiD, StackedDiD/ImputationDiD/TwoStageDiD, the plotting Notes), the migration guide, `llms-full.txt` and `docs/api/spillover.rst` describe the marking. + +- The routing rule is the M-153 LWDiD precedent (`aggregate(level).to_dataframe()` on the `AggregationMixin` classes, `build_event_study_surface(self).to_dataframe()` elsewhere); `results_base._ABSENT_SURFACE_HINTS` gains a `SunAbrahamResults` entry. Ledger: M-093 flips to `done` with `test_ref` `tests/test_v4_event_study_schema.py` (the cross-producer schema-prefix / route-equality / error-contract pins; `tests/test_v4_sentinel_retirement.py` keeps the 8a pins) and a tenth amendment note; `docs/v4-design.md` section 5 now states the PREFIX rule and item 8 is complete. REGISTRY (CS, EfficientDiD, Imputation/TwoStage/Stacked relay and reference-row Notes, DDD, HAD, plus new to_dataframe-schema Notes for dCDH, SunAbraham, WooldridgeDiD, LPDiD, DurationDiD and SpilloverDiD), the migration guide, `llms-full.txt`, the practitioner guide, `docs/api/wooldridge_etwfe.rst` and `docs/api/spillover.rst` describe the frame; notebooks 16 (re-executed, outputs refreshed and prose reconciled - it leaves the TODO.md tutorial re-execution row), 19 (reads `event_time`/`att`/`is_reference`) and 14 (export prose) updated. TODO.md gains the `level='group'` relay row. + +- Ledger row M-090 flips to `done` with `tests/test_v4_diagnostic_family_docs.py` as its `test_ref`; the test pins the estimators/diagnostics split on README, `llms.txt`, `llms-full.txt`, `docs/index.rst`, `docs/api/index.rst` and the REGISTRY archive framing, and runs in the docs-tests workflow (README-only diffs now trigger that lane). `docs/doc-deps.yaml` points the diagnostic modules at the new homepage table; the `/docs-check` classification table files Bacon as a diagnostics-class entry. + +- Ledger rows `M-016`, `M-050`..`M-058`, `M-151`, `M-152` flip to `removed` at 4.0 with + `deprecated_in: null` (no release warned); `M-148`/`M-149` flip to `removed` with + `deprecated_in: 3.13.0`; new behavior rows `M-156`/`M-157` (`tests/test_diagnostics.py`); the + ledger header, spec ladder, section 5/8/9/11 notes and the phase-5 roster carry dated amendments. + `diff_diff._deprecation` is reduced to the pickle-migration (`LEGACY_OVERALL_QUINTET`, + `migrate_renamed_state_keys`, `migrate_event_study_reference_sentinel`) and required-arg + (`NOT_SUPPLIED`, `require_arg`) helpers - `deprecated_field_property`, `resolve_renamed_kwarg`, + `warn_deprecated_kwarg` and `deprecated_kwarg_message` are deleted with their last consumers. + With the descriptors gone, assigning a legacy name to a results instance creates a shadow + attribute instead of raising (the non-slotted dataclasses) - the naming guard's consumer sweep + is the in-repo defense. Naming guard: the `DMLDiDResults.overall_att` surface entry and the + `_deprecation.py` `placebo_effects` consumer entry are deleted, the two placebo wrappers join the + rule-1 calendar `time` inventory, and the retired token families' remaining in-repo hits + (body-local `overall_att` names, the kept `__setstate__` key maps, methodology history) are + allowlisted. API autosummary stubs regenerated; TODO.md's placebo-wrapper redesign row closed; + tutorial 17 re-executed (its stored practitioner snippet echoed the old `time='post'` advice). + ## [3.13.0] - 2026-10-17 ### Added @@ -6004,6 +6603,7 @@ for the full feature history leading to this release. [2.1.2]: https://github.com/igerber/diff-diff/compare/v2.1.1...v2.1.2 [2.1.1]: https://github.com/igerber/diff-diff/compare/v2.1.0...v2.1.1 [2.1.0]: https://github.com/igerber/diff-diff/compare/v2.0.3...v2.1.0 +[4.0.0]: https://github.com/igerber/diff-diff/compare/v3.13.0...v4.0.0 [3.13.0]: https://github.com/igerber/diff-diff/compare/v3.12.0...v3.13.0 [3.12.0]: https://github.com/igerber/diff-diff/compare/v3.11.1...v3.12.0 [3.11.1]: https://github.com/igerber/diff-diff/compare/v3.11.0...v3.11.1 diff --git a/CITATION.cff b/CITATION.cff index 1d03210ca..01d79e6b9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -7,8 +7,8 @@ authors: family-names: Gerber orcid: "https://orcid.org/0009-0009-3275-5591" license: MIT -version: "3.13.0" -date-released: "2026-10-17" +version: "4.0.0" +date-released: "2026-11-01" doi: "10.5281/zenodo.19646175" url: "https://github.com/igerber/diff-diff" repository-code: "https://github.com/igerber/diff-diff" diff --git a/DEFERRED.md b/DEFERRED.md index 89a44dd22..7f43b14dc 100644 --- a/DEFERRED.md +++ b/DEFERRED.md @@ -123,7 +123,7 @@ For survey-specific limitations (`NotImplementedError` paths), see the ## Version-gated (v4) -Gated on the next major version, not on demand or derivation. Lifecycle state and +Gated on a future major version (the 4.0 cut closed 2026-11-01; no open rows), not on demand or derivation. Lifecycle state and removal/flip targets live ONLY in the CI-enforced `docs/v4-deprecations.yaml`; rows here carry the remaining implementation work and cross-link their `M-xxx` ids. @@ -143,7 +143,7 @@ decisions (refactor waivers, perf trade-offs, test-infrastructure calls) are rec |----------|----------|----------| | **`utils.validate_pscore_trim` ships without the `allow_zero` flag the retired TODO row proposed.** The row (retired in the M-145 PR) suggested `validate_pscore_trim(value, *, allow_zero)`; after ContinuousDiD's alignment to `0 < x < 0.5`, no consumer needs `allow_zero=True` — every migrated copy (TripleDifference, DMLDiD, ContinuousDiD, LWDiD, CallawaySantAnna) wants the strict interval, and the former `StaggeredTripleDifference` class's bare check went with the class (PR 4a) rather than becoming a flagged call. A dead-on-arrival parameter on a shared validator is a drift magnet; re-add the flag only when a real caller needs a `[0, 0.5)` domain, with a distinct range message | `diff_diff/utils.py` | M-145 / 2026-08-27 | | **Fixed-B Stata SE-golden comparisons in the LWDiD suite run unmarked in default CI** (no `ci_params.bootstrap()` scaling, no `slow` marker): the SE tolerance derives from BOTH fixed rep counts (ours B=999, Stata's R from the golden meta) so scaling either side would invalidate the committed-golden comparison — the ci_params convention governs convergence-style tests, not fixed-B golden comparisons — and these tests are the PR #588 acceptance bar, which must run in default CI; module-scoped fit memoization bounds the cost (~1-2 s per fit locally) | `tests/test_methodology_lwdid.py` | 2026-08-16 | -| **The 4.0 migration guide's code blocks are not snippet-executed.** `tests/test_doc_snippets.py` discovers a hardcoded list of `.rst` files and only `.. code-block:: python` / RST `::` bodies, so `docs/migration-4.0.md` gets no coverage. Deliberate: the guide is a MIXED document - most "after" examples (the renames, `results.att`) run on the current release, but the `field-flip` and `df-convention-flip` examples describe 4.0 behaviour that cannot run until 4.0, so a blanket execution lane would fail by construction. Closing the gap fully means a markdown-fence extractor plus a skip-marker convention for the future-API blocks - a harness change, out of scope for a docs PR. What IS gated: the appendix's ledger parity (`test_migration_guide_*`), which pins the row set and every mechanically checkable cell; and, since the first local review found all three merge examples carrying invalid keywords, `test_migration_guide_examples_bind_to_real_signatures`, which ast-parses the guide's python blocks and asserts every constructor/`fit()` keyword exists on the target signature. That is signature binding, NOT execution - it deliberately skips calls whose owner it cannot resolve (e.g. `results.aggregate(...)`), and it cannot catch a wrong VALUE or a wrong sequence of calls. The hand-written `Fix` prose remains unverifiable by any available means. | `docs/migration-4.0.md`, `tests/test_doc_snippets.py` | Phase 4 / 2026-08-09 | +| **The 4.0 migration guide's code blocks are not snippet-executed.** `tests/test_doc_snippets.py` discovers a hardcoded list of `.rst` files and only `.. code-block:: python` / RST `::` bodies, so `docs/migration-4.0.md` gets no coverage. Deliberate: the guide is a MIXED document - most "after" examples (the renames, `results.att`) run on the current release, but the python fences are MIXED in both directions: through 3.x the `# 4.0` halves could not run, and since 4.0.0 (2026-11-01) the `# 3.x` halves raise `ImportError` (`MultiPeriodDiD`, `StaggeredTripleDifference`, `QDiD`) and every block uses an undefined `data`, so a blanket execution lane fails by construction at every version. Closing the gap fully means a markdown-fence extractor plus a skip-marker convention for the future-API blocks - a harness change, out of scope for a docs PR. What IS gated: the appendix's ledger parity (`test_migration_guide_*`), which pins the row set and every mechanically checkable cell; and, since the first local review found all three merge examples carrying invalid keywords, `test_migration_guide_examples_bind_to_real_signatures`, which ast-parses the guide's python blocks and asserts every constructor/`fit()` keyword exists on the target signature. That is signature binding, NOT execution - it deliberately skips calls whose owner it cannot resolve (e.g. `results.aggregate(...)`), and it cannot catch a wrong VALUE or a wrong sequence of calls. The hand-written `Fix` prose remains unverifiable by any available means. | `docs/migration-4.0.md`, `tests/test_doc_snippets.py` | Phase 4 / 2026-08-09 | | **MMM launch carousel carries scoped marketing claims, not exporter-contract documentation.** The deck (a LinkedIn marketing artifact, not a docs surface) states capabilities in scoped-but-punchy form: guardrail copy says "the easy mistakes fail loudly" / "you own the design, it owns the math" (the caller-owned estimand/population/window/outcome-scale alignment lives in REGISTRY.md's MMM section and the exporter docstrings, not on slides), and slide 8 shows tutorial 29's worked staggered-boost lift row WITHOUT an on-slide linearity qualifier - the compression's linear-channel scoping is the tutorial's job, which the CTA points to. Absolutes ("no silent mis-calibration", "any estimate exports", "anything with an estimate + SE") are ban-listed by `tests/test_mmm_carousel_claims.py`; local review R1/R2 pressed for compliance-style scoping language on the slides and the marketing-appropriate scoped copy was chosen instead. | `carousel/generate_mmm_carousel.py`, `tests/test_mmm_carousel_claims.py` | mmm-carousel / 2026-08-20 | | **DCDH `sklearn.base.clone` param-identity failure won't-fix.** `ChaisemartinDHaultfoeuille._validate_paths_of_interest` unconditionally canonicalizes `paths_of_interest` into a fresh `List[Tuple[int, ...]]`, so sklearn `clone()`'s post-construction `param1 is param2` identity check fails for configured instances - a pre-existing normalization the BaseEstimator mixin PR documented rather than changed (get_params/set_params signatures are clone-compatible; the dependency-free `cls(**est.get_params())` config-equality contract is the enforced one, `tests/test_base_estimator.py`). Fixing would mean returning the caller's raw object from a validator whose job is canonicalization. | `chaisemartin_dhaultfoeuille.py` | mixin PR / 2026-08-01 | | **scikit-learn is a required dev/CI dependency, optional at runtime; clone-identity tests stay `importorskip`-guarded.** Supersedes the 2026-08-01 decision that kept scikit-learn out of the dev extra: #816 added it (unpinned) to the `dev` extra and to both `rust-test.yml` install steps, so the sklearn `clone()` round-trip tests (`test_base_estimator.py`, had/rdd/cic suites) and the DML cross-fit learner tests always run in CI. The library's runtime dependency posture is unchanged (numpy/pandas/scipy only) and the `importorskip` guards remain, so the suite still passes in an environment without scikit-learn; the always-running contract is still the dependency-free re-instantiation config-equality test. | `pyproject.toml`, `tests/test_base_estimator.py` | #816 | diff --git a/METHODOLOGY_REVIEW.md b/METHODOLOGY_REVIEW.md index 7a672f217..a57aa57f2 100644 --- a/METHODOLOGY_REVIEW.md +++ b/METHODOLOGY_REVIEW.md @@ -408,7 +408,8 @@ and covariate-adjusted specifications.) 3. **Deprecation warnings for unused parameters** (`sun_abraham.py`, `fit()`): Added `FutureWarning` for `min_pre_periods` and `min_post_periods` parameters that - are accepted but never used (no-op). These will be removed in a future version. + are accepted but never used (no-op). These were subsequently removed (neither name + is accepted by `SunAbraham.fit` any more). 4. **Removed event-time truncation at [-20, 20]** (`sun_abraham.py`): Removed the hardcoded cap `max(min(...), -20)` / `min(max(...), 20)` to match @@ -1087,8 +1088,8 @@ CI and extending covariate-adjusted R parity are tracked follow-ups in `DEFERRED 6. **Deprecated `lambda_reg` and `zeta` params; new params are `zeta_omega` and `zeta_lambda`**. The old parameters had unclear semantics and did not correspond to the paper's notation. The new parameters directly match the paper and R package - naming conventions. `lambda_reg` and `zeta` are deprecated with warnings and will - be removed in a future release. + naming conventions. `lambda_reg` and `zeta` were deprecated with warnings through + 3.x and removed at 4.0 (ledger M-001/M-002). **Outstanding Concerns:** - Cross-language parity anchor against R's default `synthdid::vcov(method="bootstrap")` @@ -1105,7 +1106,7 @@ CI and extending covariate-adjusted R parity are tracked follow-ups in `DEFERRED in REGISTRY.md §SyntheticDiD `Note (default variance_method deviation from R)`. 2. **Parameter names**: `zeta_omega` / `zeta_lambda` (matching the paper's notation); R uses `eta.omega` / `eta.lambda`. The deprecated Python aliases `lambda_reg` / `zeta` - from prior releases emit `DeprecationWarning` and will be removed in a future release. + from prior releases were removed at 4.0 (ledger M-001/M-002). --- diff --git a/ROADMAP.md b/ROADMAP.md index bfc8f6639..049d07455 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -8,9 +8,9 @@ Forward-looking plan for diff-diff, organized as queued work, candidates under c Queued work, ordered by expected leverage. Each item is its own PR. Ordering is priority-sequenced, not time-committed. -### 4.0 API unification +### 4.1 `event_study()` comparison front door -- **The 4.0 program: estimator consolidation + one API contract.** Three merges (TwoWayFixedEffects absorbs MultiPeriodDiD, TripleDifference absorbs StaggeredTripleDifference, ChangesInChanges absorbs QDiD), post-fit `results.aggregate()`, a canonical results quintet, and library-wide column/param unification - sequenced as a 3.9 shim release (new surface + FutureWarnings), 4.0 enforcement, and a 4.1 `event_study()` comparison front door. Normative spec: `docs/v4-design.md`; every queued deprecation is tracked in `docs/v4-deprecations.yaml` and enforced by CI (`tests/test_v4_matrix.py`). +- **One entry point over the staggered family.** `event_study(data, outcome, unit, time, first_treat, estimator=...)` runs the chosen staggered estimator and returns the unified event-study surface, so the estimators can be compared on one call signature. Design sketch: `docs/v4-design.md` phase 6 (sketch only; specified in its own plan). ### Practitioner-ready output diff --git a/TODO.md b/TODO.md index a1b248305..c493c87e6 100644 --- a/TODO.md +++ b/TODO.md @@ -86,7 +86,6 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m | Issue | Location | Origin | Effort | Priority | |-------|----------|--------|--------|----------| | Relay `to_dataframe(level='group')` to `aggregate('group').to_dataframe()` on CallawaySantAnna/DMLDiD, EfficientDiD and TripleDifference (staggered) - the level still raises with the post-fit pointer while `level='event_study'` was rewired to the aggregate route at the 4.0 cut (item 8b, [M-093]); ImputationDiD/TwoStageDiD keep their native `group` frames and need the same decision | `diff_diff/staggered_results.py`, `diff_diff/efficient_did_results.py`, `diff_diff/triple_diff_results.py` | #883 | Quick | Low | -| Re-execute the eight tutorials whose committed outputs still show the 3.x residual-df p-values/CIs after the `df_convention` default moved to `"cluster"` (t(G-1)): `01_basic_did`, `02_staggered_did`, `04_parallel_trends`, `05_honest_did`, `07_pretrends_power`, `09_real_world_examples`, `11_imputation_did`, `13_stacked_did` (not `16_survey_did`: the survey df wins; `16_wooldridge_etwfe` was re-executed and reconciled in the 8b PR). Run under `DIFF_DIFF_BACKEND=rust` with the per-worktree kernel, reconcile any prose that restates a moved number, and `--nbmake` each; scheduled for the 4.0 release PR (`docs/v4-design.md` section 9 item 10) together with any item-8/9 movers (24 was refreshed in #879; 23 in 7b, re-validated in 7c - it prints no p-values or CIs). 7b's label changes also drift any committed analytical DiD/TWFE summary (", G=" on the variance line) and every SunAbraham summary (the new "Std. errors:" line) - covered by the same re-execution. | `docs/tutorials/*.ipynb` | #879 | Mid | Medium | | TWFE event-study one-way HC2-BM absorbed-FE R parity against the `mpd_absorbed_fe_did` golden (`clubSandwich::vcovCR(cluster=1:n, type="CR2")`): since 4.0 (item 7b) `cluster=False` + hc2_bm reaches the one-way lane directly (no singleton-cluster column needed; `tests/test_v4_cluster_policy.py` pins finiteness and the static-surface LinearRegression parity only), so the open item is the R pin itself; the hc2 lane is pinned by `TestTWFEEventStudyAbsorbedFERParity` and the clustered hc2_bm per-period lane by `test_twfe_event_study_cluster_hc2_bm_per_period_df_matches_clubsandwich`; the MultiPeriodDiD lanes died at the 4.0 cut (PR 4c-ii) | `tests/test_estimators_vcov_type.py` | #v4-4c-i | Mid | Medium | | TWFE event-study power profile: `simulate_power`/`simulate_mde`/`simulate_sample_size` have no `_EstimatorProfile` for `TwoWayFixedEffects.fit(event_study=True)` since the MultiPeriodDiD registry entry went with the class (PR 4c-ii); `EventStudyResults` carries no scalar headline, so the profile needs a design (post-period average via the `tests/helpers/event_study_average.py` arithmetic, or a named period) plus its `_SURVEY_FIT_BUILDERS` twin | `diff_diff/power.py` | #v4-4c-ii | Mid | Low | | Clustered hc2_bm Bell-McCaffrey DOF guard is batch-relative (`linalg._cr2_bm_dof_inner`: a contrast is NaN'd when its scaled max\|B\| falls below 1e-10 of the batch maximum), so the event-study core keeps the otherwise-unused post-period-average contrast column as the anchor to hold the 3.x per-period NaN masks bit-identical (PR 4c-ii, user decision 2026-10-25). Reproduction: `x_nc = treated * 1[period >= 4] + 3e-6 * default_rng(seed).normal(size=n)` as a covariate on the oracle panel flips the mask between `[eye(k), avg]` and `eye(k)` on 20/20 seeds (cond(X) ~ 9e5; 1e-7 never flips, <= 3e-8 rank-drops) - too BLAS-sensitive for a committed oracle, hence the structural pin `test_hc2_bm_contrast_batch_keeps_post_average_anchor`. Decide whether the guard should use a batch-independent anchor (the treatment contrast, or the absolute floor only) and then drop the column | `diff_diff/estimators.py`, `diff_diff/linalg.py` | #v4-4c-ii | Mid | Medium | diff --git a/changelog.d/20261011-python-3-15.md b/changelog.d/20261011-python-3-15.md deleted file mode 100644 index 383577f7f..000000000 --- a/changelog.d/20261011-python-3-15.md +++ /dev/null @@ -1,6 +0,0 @@ -### Added -- **Python 3.15 support**: `requires-python` now allows 3.15 (`<3.16`) and - the 3.15 classifier is declared; release wheels are built for CPython 3.15 - on Linux (x86_64, aarch64), macOS arm64 and Windows, and CI runs the - Rust-backend matrix and the pure-Python fallback on 3.15. The Rust - extension builds against PyO3 0.30, which supports 3.15.0 final. diff --git a/changelog.d/20261016-reviewer-eval-gpt6-prereg.md b/changelog.d/20261016-reviewer-eval-gpt6-prereg.md deleted file mode 100644 index 6538171ac..000000000 --- a/changelog.d/20261016-reviewer-eval-gpt6-prereg.md +++ /dev/null @@ -1,28 +0,0 @@ -### Internal -- **Codex reviewer gpt-6 campaign pre-registration (PR-1 of 2)**: `tools/reviewer-eval` - pre-registers the `gpt-5.6-sol` -> `gpt-6-astra` evaluation (`DECISION_RULE.md`: - symmetric regression gate over every ground-truth bug, FP gate, downgrade rule, - verifier-confirmed leaks void a cell and contest the gate, stop conditions; the - 2026-07 rule is archived under `campaigns/`) and splits the grader-facing text into - `GRADING_RUBRIC.md` (marker-based `LEAK?` flag whose list renders from - `reviewer_audit.LEAK_RULES`). New engines: `reviewer_verdict.py` (gates, precedence, - binding to the sealed artifacts, protocol guards, PROVISIONAL/DEFINITIVE) and - `reviewer_audit.py` (post-unblinding session-log audit, informational and - over-matching by design). `run_eval.py` gains `audit` and `verdict`, runs every - codex call under an isolated CI-faithful `CODEX_HOME`, records seven-key git - provenance per artifact and in the manifest, seals artifacts at run time - (`artifacts_sha`), binds the blinded bundle (`bundle_id`, `run_ids_sha`, - rubric/prompt sha) and refuses post-run stages on a dirty or moved tree; `run` - holds an exclusive per-subdir lock for the whole attempt, records the OK cells of - aborted attempts too and refuses a rerun that would re-dispatch any cell an - earlier attempt recorded OK; review worktree leaves are salted opaque hashes. Harness CLI re-pinned to - codex-cli 0.162.1 (arm `E` = gpt-6-astra @ xhigh added; CI `codex-version` pinned - to the same, lockstep-tested with `DEFAULT_MODEL`, the plan-review pin, the - control arm and the prose pins). -- **`openai_review.py`**: `call_codex` parses codex's stderr session events into - `session_commands` (+ start/exec/other-event counts, truncation flag) on every - exit path and prints a one-line summary; `--timeout` now also caps the codex - backend's wall clock when passed explicitly (previously ignored there), `0` - means no timeout on both backends and negatives are rejected; `PRICING` - corrected for gpt-5.6-* and extended with gpt-6-*; gpt-6* counts as a - reasoning family; blinding scrubs gpt-6/astra and Unicode-dash spellings. diff --git a/changelog.d/20261017-reviewer-eval-gpt61sol-prereg.md b/changelog.d/20261017-reviewer-eval-gpt61sol-prereg.md deleted file mode 100644 index df680ccfe..000000000 --- a/changelog.d/20261017-reviewer-eval-gpt61sol-prereg.md +++ /dev/null @@ -1,23 +0,0 @@ -### Internal -- **Codex reviewer swap gpt-5.6-sol -> gpt-6.1-sol (reviewer-eval 2026-10 campaigns, PR-2)**: - two blinded, pre-registered 44-review campaigns on the frozen `tools/reviewer-eval` - harness. Campaign 1 (B = gpt-5.6-sol vs E = gpt-6-astra, both @ xhigh, k=2): mechanical - DEFINITIVE **NO-GO** on one regression (`s4-cs-unbalanced-nan-outcome`), accepted by the - user; no astra swap (`campaigns/2026-10-gpt6-VERDICT.md`). Campaign 2 (B vs F = - gpt-6.1-sol @ xhigh, k=2, fresh control sample, same rule re-issued for F): - mechanical DEFINITIVE **PARITY** with zero rule-defined regressions, zero downgrades, - 0 vs 0 false positives, two favourable catch-status changes against one - UNSTABLE-to-MISSED judgment flag, ~15% lower latency and half the input price; the - user accepted PARITY as GO - (`campaigns/2026-10-gpt61sol-VERDICT.md`). Both verdicts ship with their redacted - grading records (`campaigns/2026-10-gpt6*-grading/`). Production pins move in lockstep - to `gpt-6.1-sol`: the CI workflow `model`, `openai_review.DEFAULT_MODEL` and the - plan-review `CODEX_MODEL` (carried over from the code-review campaign, not re-run - through plan-review-eval; TODO.md row), with the plan-review `CODEX_TIMEOUT_S` raised - 2400 -> 3600 to equal the harness ceiling. Harness: arm `F` registered with the contrast - `(B, F)`, campaign 1's rule archived as `campaigns/2026-10-gpt6-DECISION_RULE.md`, - `run --configs` defaults to the last registered shape, `PRICING` gains `gpt-6.1-sol`, - arm F takes `role=control` (B retained as previous production), the candidate-exposure - gate exempts the control arm so bare `smoke` keeps working after a swap, and the - DEFERRED `gpt-6-sol` cost-fallback row is retired (codex-cli 0.162.1's ChatGPT-account - login accepts the gpt-6 Sol tier; campaign 2 was that evaluation). diff --git a/changelog.d/20261018-npm-lock-security-refresh.md b/changelog.d/20261018-npm-lock-security-refresh.md deleted file mode 100644 index 1b039a4c7..000000000 --- a/changelog.d/20261018-npm-lock-security-refresh.md +++ /dev/null @@ -1,10 +0,0 @@ -### Security -- **CI smoke-test lockfile refresh**: bumped the transitive `undici` (7.29.0 -> 7.30.1) - and `source-map-js` (1.2.1 -> 1.2.2) pins in `.github/scripts/package-lock.json`, - clearing all seven open Dependabot alerts. CI-only (docs search-excerpt smoke test); - nothing in the published wheel is affected. - -### Internal -- **Dependabot npm coverage**: added an `npm` ecosystem entry for `/.github/scripts` - so the smoke-test toolchain gets weekly version-update PRs (grouped minor/patch) - instead of drifting into advisories between manual lockfile refreshes. diff --git a/changelog.d/20261018-v4-cut-framing.md b/changelog.d/20261018-v4-cut-framing.md deleted file mode 100644 index 4dc4255dc..000000000 --- a/changelog.d/20261018-v4-cut-framing.md +++ /dev/null @@ -1,29 +0,0 @@ -### Documentation -- **4.0-cut framing** (the first PR of the 4.0 cut, at v3.13.0): `docs/v4-design.md` - re-synced with the library as shipped since 3.9 - the estimator roster now counts - LWDiD/DMLDiD/DurationDiD (27 -> 24 at 4.0, 23 estimators + Bacon in the diagnostic - family), the diagnostic family names the TWFE weights containers and - `DurationDiDPretestResults`, and section 9 carries the ten-PR cut sequence. `M-080` - (auto-cluster policy) re-scoped to the measured roster: static `TwoWayFixedEffects` - already auto-clusters at unit, `SpilloverDiD` is the one estimator whose default SE - moves at 4.0, `cluster=False` becomes the uniform explicit off-switch, and the - by-construction estimators keep no auto-cluster metadata by decision (new "Default - clustering roster" section in `docs/methodology/variance-conventions.md`; REGISTRY - Notes on SpilloverDiD, StackedDiD, ImputationDiD, TwoStageDiD, DMLDiD, DurationDiD). - `M-008` (`DIFF_DIFF_SOLVE_OLS_FASTPATH` default) decided: evaluated, kept off. New - ledger row `M-150` gives `WooldridgeDiDResults.to_dataframe(level="event")` its own - locator (twin of `M-136`). The migration guide's auto-cluster section, appendix and - orientation table follow the ledger (111 appendix rows). - -### Internal -- **Ledger cut window** (`docs/v4-deprecations.yaml` header `cut_window: "4.0"`, - `cut_base: "3.13.0"`; spec section 11): rows scheduled at 4.0 may flip terminal - ahead of the version bump so the removals land over many PRs while `__version__` - stays 3.13.0; `tests/test_v4_matrix.py` relaxes only the early-removal guard for the - window version, refuses any other version bump while early-terminal rows exist, and - keeps due enforcement unchanged. The id snapshot and `ROW_COUNT_FLOOR` now include - `M-148`/`M-149` (shipped by #838 without extending them) and `M-150` (132 rows). - `M-091`'s roster records the four later-born `Diagnostic` containers; `M-050`'s - `code_refs` record that `diff_diff/_deprecation.py` survives 4.0 as pickle-migration - infrastructure; `M-045`/`M-115` name the legacy-CR1 remap branches their removal - deletes. A `maint/3.13` branch is cut at `v3.13.0` for 3.13.x patches. diff --git a/changelog.d/20261018-v4-pr1-conformance.md b/changelog.d/20261018-v4-pr1-conformance.md deleted file mode 100644 index cb953ba11..000000000 --- a/changelog.d/20261018-v4-pr1-conformance.md +++ /dev/null @@ -1,28 +0,0 @@ -### Added -- **`LWDiDResults.summary(alpha=None)` / `print_summary(alpha=None)`** join the family-wide - summary-alpha guard (ledger row `M-146`): a value other than the fit-time `alpha` raises - `ValueError` instead of being ignored; the methods previously took no `alpha` at all. -- **`LWDiDResults.to_dataframe(level=...)` / `to_csv(path, level=...)`** gain the uniform - `level` selector (`"simple"` default, `"group"`, `"event_study"`) - see Breaking Changes. -- **`DurationDiDResults.aggregate("simple")`**: a verbatim relay of the stored headline - quintet (`n = n_units`, `df` NaN - inference is bootstrap-only), joining the spec section 6 - `simple` contract every aggregation adopter honours. -- **`RDDensityTestResult.to_dict()`**: JSON-serializable scalars with native casts (NaN - inference passes through), the `*_conventional` block `None` unless `report_all=True`, and - the bandwidth-selection table nested under `bandwidths` only when it was estimated. - -### Deprecated -- **`LWDiDResults.pvalue` and `LWDiDResults.ci`** (ledger rows `M-151`, `M-152`): the - 3.10-born alias properties for `p_value` / `conf_int` were first shimmed with a `FutureWarning` - and then removed outright at 4.0 (item 9b of the cut). Read `.p_value` / `.conf_int`. - -### Breaking Changes -- **`LWDiDResults.to_dataframe()` is reshaped onto the shared results schema** (ledger row - `M-153`): each `level` returns `aggregate(level).to_dataframe()` - columns `level, label, - target, att, se, t_stat, p_value, conf_int_lower, conf_int_upper, n, weight, df` (the - event-study schema for `level="event_study"`) - plus LWDiD's `rolling`, `estimation_method` - and `vcov_type` columns. The default `"simple"` is ONE headline row; the former per-cohort - rows are `level="group"` (without the old trailing `"Overall"` row). The `term` / `cohort` - column is now `label`, `n_treated` is the schema's `n`, `n_obs` and `n_control` are no longer - in the frame (both stay in `to_dict()`), and `ci_lower` / `ci_upper` are - `conf_int_lower` / `conf_int_upper`. `to_csv()` follows and takes the same `level`. diff --git a/changelog.d/20261018-v4-pr2-shim-removals.md b/changelog.d/20261018-v4-pr2-shim-removals.md deleted file mode 100644 index 2f8e3e6f2..000000000 --- a/changelog.d/20261018-v4-pr2-shim-removals.md +++ /dev/null @@ -1,60 +0,0 @@ -### Removed -- **The 3.9 rename shims are gone (ledger rows flipped to `removed`).** Old keyword spellings - now raise `TypeError`: `DifferenceInDifferences.fit(time=)` -> `post=` (`M-030`), - `permutation_test(time=)` / `leave_one_out_test(time=)` -> `post=` (`M-137`, `M-138`), - `WooldridgeDiD.fit(cohort=)` -> `first_treat=` (`M-032`), `ChaisemartinDHaultfoeuille.fit(group=, - controls=)` -> `unit=`, `covariates=` (`M-033`, `M-034`), `twowayfeweights(group=)` -> `unit=` - (`M-097`), the `*_col` spellings on `HeterogeneousAdoptionDiD.fit`, `RegressionDiscontinuity.fit`, - `RDPlot.fit`, `joint_pretrends_test`, `joint_homogeneity_test`, `did_had_pretest_workflow` - (`M-035`..`M-042`, `M-088`, `M-089`, `M-098`..`M-112`) and `trim_weights(weight_col=)` -> - `weights=` (`M-113`), `StackedDiD(clean_control=)` -> `control_group=` (`M-043`), - `WooldridgeDiDResults.to_dataframe(aggregation=)` -> `level=` (`M-044`) and - `summary(aggregation)` (`M-087`; `summary(alpha=None)` is now the positional signature), - `ContinuousDiD(covariates=)` -> `fit(covariates=)` (`M-084`). The `"event"` value spelling is - rejected with `ValueError` on `WooldridgeDiDResults.aggregate(type=)`, `.to_dataframe(level=)` and - `LPDiDResults.to_dataframe(level=)` - use `"event_study"` (`M-086`, `M-150`, `M-136`). -- **`robust=` removed** from `DifferenceInDifferences` (and so `TwoWayFixedEffects`, - `MultiPeriodDiD`), `TripleDifference`, `HeterogeneousAdoptionDiD` and `LinearRegression` - (`M-045`..`M-047`, `M-115`); `vcov_type=` is the single selector. The legacy-CR1 remap that - turned `robust=False` + clustering into `hc1` went with it. Positional constructor slots after - the removed parameter shift left by one (DiD slot 1, LinearRegression/TripleDifference slot 2, - HeterogeneousAdoptionDiD slot 6); `SyntheticDiD`'s inert `lambda_reg`/`zeta` (`M-001`, `M-002`, - constructor slots 7-8) are removed too, shifting `vcov_type` and the `conley_*` parameters two - slots left. Pass these by keyword. -- **Results-field aliases removed**: `SyntheticDiDResults.placebo_effects` (`M-003`; use - `variance_effects`), `RegressionDiscontinuityResults.treatment_col` (`M-094`; `takeup`), - `StackedDiDResults.clean_control` and `StackedDiD.clean_control` (`M-095`, `M-043`; - `control_group`), `ChaisemartinDHaultfoeuilleResults.groups` (`M-114`; `units`), - `ChangesInChangesResults.estimator` (`M-143`; `method`). Their serialized dual keys are gone from - `to_dict()` (`treatment_col`, `clean_control`, `estimator`, Wooldridge's - `aggregation_weights["event"]`); 3.x pickles still load (`__setstate__` key migrations kept). -- **The eight module-level wrapper functions are removed** (`M-070`..`M-077`): `imputation_did`, - `two_stage_did`, `stacked_did`, `trop`, `synthetic_control`, `triple_difference`, - `bacon_decompose`, `chaisemartin_dhaultfoeuille`. Call the estimator classes. `diff_diff.trop`, - `diff_diff.synthetic_control`, `diff_diff.stacked_did` and `diff_diff.chaisemartin_dhaultfoeuille` - now name the submodules (as `diff_diff.bacon` always did). -- **The dieted aliases are removed** (`M-132`..`M-134`): `CDiD` -> `ContinuousDiD`, `Stacked` -> - `StackedDiD`, `Gardner` -> `TwoStageDiD`; the module `__getattr__` warning shim (`M-135`) is gone. - -### Changed -- **`WooldridgeDiDResults.summary(alpha=None)`** renders the headline (simple) row only; the - group / calendar / event-study tables it used to render on request - and their `[pre]`/`[antic]` - relative-period labels - are no longer part of `summary()`. Use `aggregate(...)` / - `to_dataframe(level=...)`; `anticipation` is still reported on the results object. - `to_dataframe(level=)` now rejects an unknown level with `ValueError` instead of returning an - empty frame. -- `practitioner_next_steps` no longer recognises duck-typed results that carry only the legacy - `estimator` attribute; `ChangesInChangesResults.method` is the routing field. - -### Breaking Changes -- **`BusinessReport.to_dict()` schema `2.0` -> `3.0`**: the StackedDiD `assumption` block no longer - carries the legacy `clean_control` key (read `control_group`). `DiagnosticReport`'s schema stays - `2.0`. - -### Internal -- `tests/test_v4_matrix.py` removed-status probes: a function row that resolves to a submodule - counts as absent; a param row absent from the explicit signature counts as removed despite - `**kwargs`. Naming-guard allowlist extended for the kept body-local aliases, `__setstate__` keys - and shared vocabulary. The `diff_diff._deprecation` helpers survived this PR for the remaining - M-031/M-082 and PR-3 shims (item 9b later reduced the module to its pickle-migration and - required-arg helpers). diff --git a/changelog.d/20261020-v4-pr3a-aggregate-test-migration.md b/changelog.d/20261020-v4-pr3a-aggregate-test-migration.md deleted file mode 100644 index 5c44f562e..000000000 --- a/changelog.d/20261020-v4-pr3a-aggregate-test-migration.md +++ /dev/null @@ -1,25 +0,0 @@ -### Internal -- **4.0 cut, PR 3a - tests-first migration to the post-fit aggregation containers.** The - test and benchmark suites now read event-study / group surfaces from - `results.aggregate("event_study")` / `results.aggregate("group")` (the container API that - has existed since 3.9) instead of the deprecated fit-time `fit(aggregate=...)` keyword and - the legacy `event_study_effects` / `group_effects` fields, so the keyword removals in PRs 3b - and 3c land against already-migrated suites. No library source changes and no ledger row - flips in this PR; the fit-time keywords still work and still warn. Bootstrapped - ImputationDiD / TwoStageDiD / ContinuousDiD sites stay on the fit-time route until PR 3c - adds the post-fit relay for them. -- **Committed per-backend aggregation oracles.** `tests/test_aggregate_contract.py` no longer - uses the fit-time route as its numerical reference: every analytic surface is pinned at - `rtol=1e-12` (confidence-interval bounds at `1e-9`: scipy's `t.ppf` moved by up to 3.4e-11 - relative between 1.13 and 1.14+) and every bootstrapped CS / EfficientDiD replay at the - replay-parity tolerances against `tests/fixtures/v4_aggregate_oracles.json`, captured on the unmodified - tree at `8724fecd` by `tests/_capture_v4_aggregate_oracles.py` under both weight backends - (see `tests/fixtures/README.md`). New `tests/helpers/legacy_surface.py` (`es_dict`, - `group_dict`, `legacy_standin`) gives consumer tests a faithful stand-in for the 3.x - fit-time surface where their subject is the native (legacy-pickle) route. -- **Benchmarks** (`benchmarks/python/benchmark_callaway.py`, `benchmark_imputation.py`, the - `speed_review` scripts) call `aggregate()` post-fit inside the timed region and emit the - same JSON row shape the R refresh joins on. -- `docs/v4-design.md` section 9 records the three-way split of cut-sequence item 3 (3a - tests-first, 3b mechanical removals, 3c the Imputation/TwoStage/ContinuousDiD removals + - relay). diff --git a/changelog.d/20261021-v4-pr3b-aggregate-removals-mechanical.md b/changelog.d/20261021-v4-pr3b-aggregate-removals-mechanical.md deleted file mode 100644 index d559f3736..000000000 --- a/changelog.d/20261021-v4-pr3b-aggregate-removals-mechanical.md +++ /dev/null @@ -1,55 +0,0 @@ -### Removed -- **The fit-time `aggregate=` / `balance_e=` keywords are gone on the estimators whose post-fit - route already existed (ledger rows flipped to `removed`).** `fit(aggregate=)` is removed from - `CallawaySantAnna` (`M-020`), `EfficientDiD` (`M-023`), `StackedDiD` (`M-024`), - `ChaisemartinDHaultfoeuille` (`M-026`) and `HeterogeneousAdoptionDiD` (`M-027`), - `fit(balance_e=)` from `CallawaySantAnna` (`M-117`) and `EfficientDiD` (`M-120`), and - `aggregate=` from `did_had_pretest_workflow` (`M-139`; the `HADPretestReport.aggregate` - output field survives). The successor is post-fit `results.aggregate(type, balance_e=)`; HAD - selects its mode from the panel shape. With the keywords go StackedDiD's fit-time - `group`/`all`/unknown-value `ValueError`s (`aggregate('group')` already fails closed in the - mixin) and the private `_VALID_AGGREGATES` sets in `had.py` / `had_pretests.py` - (`_infer_aggregate_mode` is the sole selector). `ImputationDiD`, `TwoStageDiD` and - `ContinuousDiD` keep their deprecated keywords until the next cut PR adds the - bootstrapped-fit relay. -- **CallawaySantAnna and EfficientDiD compute no event-study/group surface at fit.** Their - legacy `event_study_effects` / `group_effects` fields - and CS's `cband_crit_value`, - `event_study_vcov`, `event_study_vcov_index`, `event_study_df` - are `None` after EVERY fit; - the post-fit replay (`aggregate('event_study'|'group', balance_e=)`) is the only bootstrap - route and is unchanged. - -### Breaking Changes -- Passing `aggregate=` or `balance_e=` to `CallawaySantAnna.fit`, `EfficientDiD.fit`, - `StackedDiD.fit`, `ChaisemartinDHaultfoeuille.fit` or `HeterogeneousAdoptionDiD.fit` (or - `aggregate=` to `did_had_pretest_workflow`) now raises Python's native - `TypeError: ... unexpected keyword argument`. `TripleDifference`'s staggered-mode - `aggregate=`/`balance_e=` are untouched (`M-140`/`M-141`, canonical until the DDD container - unification). -- **Positional slots after the removed parameters shift left.** `CallawaySantAnna`: - `survey_design` 9 -> 7; `EfficientDiD`: `survey_design` 9 -> 7, `store_eif` 10 -> 8; - `StackedDiD` (`aggregate` sat before them): `population`, `survey_design`, `covariates` - 7/8/9 -> 6/7/8; `ChaisemartinDHaultfoeuille`: `L_max`, `covariates`, `trends_linear`, - `trends_nonparam`, `honest_did`, `heterogeneity`, `design2`, `survey_design` each move one - slot left (7..14 -> 6..13). `HeterogeneousAdoptionDiD.fit` shifts nothing (everything after - the removed slot is keyword-only; a seventh positional now raises). Pass these by keyword. -- `CallawaySantAnnaResults.to_dataframe(level="event_study"|"group")` and the - `EfficientDiDResults` twins raise on every fresh fit (the legacy fields are always `None`); - use `results.aggregate(...).to_dataframe()`. The column-schema rewire of these levels onto the - container is `M-093`'s (PR 8). - -### Changed -- Runtime messages, the four `llms*.txt` guides, the API docs, the migration guide and the - benchmarks no longer steer users to the fit-time route on these five estimators; - `CallawaySantAnna`'s class docstring, `plot_event_study`, `honest_did` / `pretrends` and the - results-class "not stored" messages point at `results.aggregate('event_study')`. - -### Internal -- `tests/test_aggregate_contract.py`: the `Test*Shim` classes, fit-time fixtures and - inertness comparisons for the five estimators are retired in favour of `Test*Removal` - pins (TypeError, signature, positional-slot binding); the committed oracle fixture is the - numerical reference. `tests/_capture_v4_aggregate_oracles.py` is marked historical (it - drives the removed route; runnable only at the recorded `captured_at_head`). Naming-guard - allowlist entries for the `llms.txt` / `llms-full.txt` canonical post-fit `aggregate` - vocabulary. Ledger row `M-093` records that CS/EfficientDiD populate the legacy fields on - no fit. `warn_deprecated_kwarg` loses its last library caller and is retained (spec section - 8 rule 11) with a direct unit pin. diff --git a/changelog.d/20261021-v4-pr3c-aggregate-relay.md b/changelog.d/20261021-v4-pr3c-aggregate-relay.md deleted file mode 100644 index e30108702..000000000 --- a/changelog.d/20261021-v4-pr3c-aggregate-relay.md +++ /dev/null @@ -1,85 +0,0 @@ -### Removed -- **The last fit-time `aggregate=` / `balance_e=` keywords are gone (ledger rows flipped to - `removed`).** `fit(aggregate=)` is removed from `ImputationDiD` (`M-021`), `TwoStageDiD` - (`M-022`) and `ContinuousDiD` (`M-025`), `fit(balance_e=)` from `ImputationDiD` (`M-118`) - and `TwoStageDiD` (`M-119`). With PR 3b's removals on CallawaySantAnna / EfficientDiD / - StackedDiD / dCDH / HAD this completes the aggregate-postfit group: every adopter aggregates - through post-fit `results.aggregate(type, balance_e=)` only, and `TripleDifference`'s - staggered-mode keywords are the one surviving fit-time `aggregate=` (`M-140`/`M-141`, - canonical until the DDD container unification). With the keywords go ContinuousDiD's - no-underscore `"eventstudy"` spelling and its fit-time value validation, the dead analytic - fit-time event-study inference arm in `continuous_did.py`, and ImputationDiD's unreachable - pretrends+replicate fit-time gate. - -### Breaking Changes -- Passing `aggregate=` or `balance_e=` to `ImputationDiD.fit`, `TwoStageDiD.fit` or - `ContinuousDiD.fit` now raises Python's native `TypeError: ... unexpected keyword argument`. -- **Positional slots after the removed parameters shift left.** `ImputationDiD` and - `TwoStageDiD`: `survey_design` 9 -> 7; `ContinuousDiD`: `survey_design`, `covariates` - 8/9 -> 7/8. Pass these by keyword. -- `aggregate('event_study', balance_e=k)` on a BOOTSTRAPPED `ImputationDiD` / `TwoStageDiD` - fit raises `NotImplementedError` (the relayed surface was computed unrestricted; re-fit with - `n_bootstrap=0` for a balanced window). Analytic fits recompute with `balance_e` as before. -- A bootstrapped `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` fit WITH bootstrap inference - but WITHOUT a stored surface - a 3.9-3.13 bootstrapped plain-fit pickle, or a fit whose - fail-soft materialization dropped it - raises `ValueError` from `aggregate('event_study')` / - `aggregate('group')` (previously `NotImplementedError` from the bootstrap gate); the message - names the refit. A 3.9-3.13 bootstrapped pickle fitted with `balance_e=k` stores a restricted - surface and the relay returns it as stored. - -### Changed -- **Bootstrapped `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` fits relay their event-study - (and, on Imputation/TwoStage, group) surface.** The deprecated keyword was the only route to - bootstrap event-study inference there (no per-draw state is retained for a replay), so a fit - with `n_bootstrap > 0` now ALWAYS computes those surfaces at fit - exactly what - `fit(aggregate="all")` computed, `balance_e=None` - stores them in the legacy - `event_study_effects` / `group_effects` fields, and `aggregate('event_study')` / - `aggregate('group')` RELAYS them (kit-snapshot alpha / anticipation / survey metadata; NaN - `df` column; `vcov` `None`) instead of raising `NotImplementedError`. Analytic fits are - unchanged (legacy fields `None`, kit recompute on demand). Storage rule: the legacy fields - hold a surface IFF the fit carries bootstrap inference. On Imputation/TwoStage a bootstrapped - fit that ends analytical (zero valid draws, total bootstrap failure) clears them and - `aggregate()` recomputes analytically. ContinuousDiD's bootstrapped kit is scalars-only, so - no analytical fallback exists there: a bootstrapped fit with no post-treatment cells never - runs its bootstrap (overall ATT already NaN with a `UserWarning`), stores no surface, and - `aggregate('event_study')` raises the absent-surface `ValueError`. - - Materialization is FAIL-SOFT with a one-shot overall-only retry: an exception while - building the surfaces, or a bootstrap attempt that raises / returns `None` with surfaces - present, leaves them `None`, emits one `UserWarning` ("... could not be materialized ...") - and re-runs the bootstrap overall-only (a seeded fit reproduces the first attempt's weight - draws), so a plain bootstrapped fit's headline quintet and its success/abort outcome are - unchanged by the removal. ContinuousDiD bootstrap failures still propagate as before. - - Consequences on bootstrapped plain fits: the aggregators' own `UserWarning`s (ImputationDiD - Prop-5 unidentified horizons, empty horizons, LOO single-unit auxiliaries; TwoStageDiD - Stage-1/Stage-2 singular or rank-deficient fallbacks) and the per-target bootstrap-stats - `RuntimeWarning`s now fire where a plain fit never emitted them; `summary()` prints the - event-study/group tables and `to_dataframe(level="event_study"|"group")` returns frames - instead of raising; `DiagnosticReport` / `BusinessReport` run heterogeneity (Imputation / - TwoStage on the relayed group dict, ContinuousDiD on the event study) and parallel trends - (Imputation / TwoStage) on the relayed surface instead of skipping with - `NotImplementedError`; `plot_event_study(results)` plots them natively. Measured cost (199 - draws, 300 units x 12 periods): Imputation 0.01s -> 0.08s, TwoStage 0.01s -> 0.04s, - Continuous unchanged at 0.05s; all three default to `n_bootstrap=0`. -- `ImputationDiD`'s inert-`df_convention` warning predicate is now exact: the per-lead inference - is reachable iff `pretrends=True` and the design is not replicate-weighted (`M-127`); the - unreachable pretrends+replicate fit-time gate is deleted (the combination is still rejected - post-fit by `aggregate('event_study')`). -- Runtime messages (`plot_event_study`, the results-class "not stored" hints, - `_reporting_helpers`, `DiagnosticReport` docstrings), `practitioner_next_steps` advice for - bootstrapped ImputationDiD / TwoStageDiD / ContinuousDiD fits, the `llms*.txt` guides, the - ContinuousDiD API page and tutorial, the migration guide and the performance docs describe - the relay instead of steering users to the removed fit-time route. - -### Internal -- `tests/test_aggregate_contract.py`: the remaining `Test*Shim` classes, fit-time fixtures and - fail-closed bootstrap pins are retired for `Test*Removal` pins (TypeError, signature, - positional-slot binding) and relay pins - the relayed surfaces equal the stored legacy dicts - row-for-row and match the per-backend bootstrap oracles - plus failure-injection pins for the - fail-soft materialization and the storage rule. The estimator suites, `DiagnosticReport`, - `event_study_surface` (bootstrapped Imputation / TwoStage / Continuous enrolled as genuine - native producers) and practitioner tests take their relay pins; the module-level - FutureWarning filters for the fit-time route are gone. Naming-guard allowlist entries for the - guides' post-fit `aggregate` vocabulary are finalized. Ledger row `M-093` gains the fit-side - table of which producers populate the legacy fields; `M-127` records the exact predicate. - `docs/v4-design.md` section 6 carries the relay amendment and the HYBRIDS paragraph; section 9 - marks sub-item 3c shipped. diff --git a/changelog.d/20261021-v4-pr4a-std-qdid-removals.md b/changelog.d/20261021-v4-pr4a-std-qdid-removals.md deleted file mode 100644 index 04c6b2677..000000000 --- a/changelog.d/20261021-v4-pr4a-std-qdid-removals.md +++ /dev/null @@ -1,42 +0,0 @@ -### Removed -- **The `StaggeredTripleDifference` class and its `SDDD` alias are gone** (`M-013`, `M-064`; 4.0 - cut, PR 4a). `from diff_diff import StaggeredTripleDifference` and `SDDD` raise `ImportError`, - and the `diff_diff.staggered_triple_diff` module no longer exists. The staggered DDD estimator - is `TripleDifference().fit(..., unit=, time=, first_treat=, partition=)` - the same engine - (`diff_diff/_staggered_triple_diff_engine.py`), pinned against the committed oracle in - `tests/test_v4_merge_ddd.py`. R's compact `control_group` spellings - (`"notyettreated"`/`"nevertreated"`) died with the class; the merged surface takes - `"not_yet_treated"`/`"never_treated"`. -- **The `QDiD` class and the `QDiDResults` alias are gone** (`M-015`, `M-061`). The quantile-DiD - comparison estimator is `ChangesInChanges(method="qdid")` (same engine, identical numbers); - the results container is `ChangesInChangesResults` for both methods. -- The two `pyproject.toml` pytest `filterwarnings` entries that silenced the removed classes' - construction `FutureWarning`s. - -### Breaking Changes -- **`TripleDifference.fit(time=)` in the 2x2x2 design now raises `ValueError`** naming `post=` - (`M-085`). Through 3.x `time=` was the deprecated alias for the 0/1 post indicator and emitted - a `FutureWarning` (`M-031`); since 4.0 `time` means the staggered calendar column only. - Supplying both `post=` and `time=` keeps raising the "pass only post=" error. -- Keyword mapping for callers of the removed class: `eligibility=` is `partition=` on - `TripleDifference`; the staggered fit parameters are keyword-only; `cluster=` raises in - staggered mode (use `n_bootstrap > 0` for unit-level clustered inference). Users of - `balance[did]` stay on 3.x through that extra's `diff-diff<4` pin, where the old name still - resolves. - -### Internal -- The three StaggeredTripleDifference suites (`tests/test_staggered_triple_diff.py`, - `tests/test_methodology_staggered_triple_diff.py`, `tests/test_survey_staggered_ddd.py`) are - ported to `TripleDifference`'s staggered mode (keyword fits; the R golden's compact - `control_group` values are mapped), so the engine keeps its behavior, methodology and survey - coverage. `tests/test_v4_merge_ddd.py` / `tests/test_v4_merge_cic.py` drop their old-vs-new - parity lanes for `Test*Removal` pins; the two capture scripts are marked historical and - `make_2x2` lives in the CiC merge test. -- Ledger: `M-013`/`M-015` -> `removed`, `M-061`/`M-064`/`M-085` -> `done`; `M-031` keeps - `shimmed` with `warning: null`; `M-080` drops the deleted module from its `code_refs`. - Spec: cut-sequence item 4 split into 4a/4b/4c, the cross-row rule gains its alias clause. - `docs/doc-deps.yaml` re-primes the `staggered_triple_diff` group on the results module. -- `tests/test_v4_matrix.py` no longer requires `StaggeredTripleDifference`/`QDiD` worked-example - owners in the migration guide; the two autosummary stubs are deleted; REGISTRY keeps the - `## StaggeredTripleDifference` and `### QDiD` sections as the engine's methodology record - (archive framing lands with `M-090`). diff --git a/changelog.d/20261023-v4-pr4b-tutorials-twfe-es.md b/changelog.d/20261023-v4-pr4b-tutorials-twfe-es.md deleted file mode 100644 index 90e818124..000000000 --- a/changelog.d/20261023-v4-pr4b-tutorials-twfe-es.md +++ /dev/null @@ -1,29 +0,0 @@ -### Documentation -- **Tutorials 02/04/05/07/23 are rebuilt on `TwoWayFixedEffects().fit(..., event_study=True)`** - (`spec="within"`: unit + period fixed effects, unit-clustered SEs) ahead of the - MultiPeriodDiD removal (`M-010`, 4.0 cut PR 4c). Point estimates match the former - MultiPeriodDiD fits on these balanced panels; the SEs change because the within spec - clusters by unit. Tutorial 23's naive headline is the equal-weight average of the two - post-period coefficients computed in the cell (ATT unchanged at -4.29; the SE is now the - unit-clustered one), and tutorial 02's period-effects loop reads `to_dataframe()`. -- The static `TwoWayFixedEffects().fit(..., time=...)` calls in tutorials 01/02/09 (the - `M-082` alias, misused there as the post dummy) are replaced: 01 passes `post="post"`; - 02 and 09 report `TwoWayFixedEffects().decompose(...).twfe_estimate`, the unit + period - fixed-effects coefficient their Goodman-Bacon sections decompose (the old cells printed - `treated x period` artefacts: 0.6309 and 0.0001). Tutorial 02 cell 36's covariate draw is - seeded. Tutorials 02 and 09 are re-executed on the Rust backend, which produced the - committed outputs, so the seeded bootstrap cells are unchanged. -- Tutorials 08 and 16 describe `StaggeredTripleDifference` as the removed class (4.0 cut, - PR 4a). `diff_diff/guides/llms-full.txt`'s event-study snippet calls `print(es.summary())` - (`EventStudyResults` has no `print_summary`). - -### Testing -- `tests/test_t23_spillover_tva_drift.py`'s `naive_fit` fixture mirrors the notebook's TWFE - event-study headline cell, runs the fit with warnings escalated to errors, and pins the - post-period-average identity and the unit-clustered vcov-block SE alongside the existing - -4.29 endpoint. - -### Internal -- `docs/v4-design.md` cut-sequence item 4b marked shipped; item 4c lists the two remaining - markdown-only MultiPeriodDiD mentions (06 cell 34, 16 cell 40). `docs/doc-deps.yaml` maps - the TWFE module to tutorials 01/02/04/05/07/09/23. No ledger row flips. diff --git a/changelog.d/20261024-v4-pr4c-i-twfe-es-decoupling.md b/changelog.d/20261024-v4-pr4c-i-twfe-es-decoupling.md deleted file mode 100644 index 56ac2d705..000000000 --- a/changelog.d/20261024-v4-pr4c-i-twfe-es-decoupling.md +++ /dev/null @@ -1,26 +0,0 @@ -### Internal -- **The TWFE event-study surface is built directly from the shared core's payload** - (4.0 cut, PR 4c-i; ledger rows `M-010` / `M-011`): `DifferenceInDifferences._fit_event_study_core` - returns a private `_EventStudyCorePayload`, `TwoWayFixedEffects.fit(..., event_study=True)` - builds its `EventStudyResults` from it via `results_base._from_event_study_payload`, and only - `MultiPeriodDiD.fit` still wraps the payload into `MultiPeriodDiDResults`. `_from_mpd` and the - payload route share one calendar-surface builder, so they cannot drift; a committed oracle - captured on the pre-refactor tree (`tests/fixtures/v4_twfe_event_study_oracles.json`, capture - script `tests/_capture_v4_merge_mpd_oracles.py`) pins every field of the TWFE surface and of - its MultiPeriodDiD twin across eleven variance lanes. No user-visible behavior changes. -- Spec item 4c is split into 4c-i (this PR) and 4c-ii (the removals); the 4c-ii bullet records - the `spec="within"` benchmark-arm relabel and the `M-016` view's record type (user decisions - 2026-10-24). Ledger `M-010` / `M-011` notes updated; no status flips. - -### Testing -- The MultiPeriodDiD-fixture suites (honest_did, pretrends, methodology pretrends/honest, - visualization, Conley, survey, replicate weights, vcov_type, df_convention, the estimators - event-study behaviour pins, the event-study surface roster, the DiagnosticReport / - BusinessReport generic fixtures) now run on `TwoWayFixedEffects().fit(..., event_study=True)` - (or a static TWFE fit for the report classes); MultiPeriodDiD's own tests, the MPD-only - designs (multi-`absorb`, `fixed_effects=`, wild bootstrap, container-field assertions, the - MPD-branch consumer tests) and the MPD-vs-TWFE parity oracles are unchanged until PR 4c-ii. - New `tests/test_twfe_event_study_decoupling.py`, shared helpers - `tests/helpers/twfe_es_oracle_lanes.py`, `event_study_average.py` and - `event_study_surface_builders.py`; the variance-conventions inventory gains the two TWFE - event-study rows; a TODO row tracks the one-way HC2-BM absorbed-FE R-parity lane. diff --git a/changelog.d/20261025-v4-pr4c-ii-b-docs-benchmarks.md b/changelog.d/20261025-v4-pr4c-ii-b-docs-benchmarks.md deleted file mode 100644 index 23a630097..000000000 --- a/changelog.d/20261025-v4-pr4c-ii-b-docs-benchmarks.md +++ /dev/null @@ -1,12 +0,0 @@ -### Internal -- PR 4c-ii-b (docs/benchmarks half of the MultiPeriodDiD removal): REGISTRY / REPORTING / - survey-theory / METHODOLOGY_REVIEW and the remaining user-doc prose stop describing the - removed class as live; TODO rows 33/35/38/95/99 and three DEFERRED rows that governed the - class are reworded or retired. The oracle fixture `tests/fixtures/v4_twfe_event_study_oracles. - json` drops its `mpd_twin` records and the lane metadata loses its MPD twin fields (the TWFE - records are byte-identical); the capture script documents the per-lane capture SHAs. -- Benchmarks: the live event-study arm is the new `benchmarks/python/benchmark_twfe_event_study.py` - (`spec="within"`, `cluster="unit"`; parity against R `fixest::feols(... | unit)` verified - locally - ATT to 2e-12, SE to 0.0%, per-period effects correlation 1.000); - `benchmark_multiperiod.py` and `benchmarks/refresh_2026_07/` stay frozen because that harness - runs inside pinned 3.5.3/3.7.0 venvs that predate `event_study=True`. diff --git a/changelog.d/20261025-v4-pr4c-ii-remove-mpd.md b/changelog.d/20261025-v4-pr4c-ii-remove-mpd.md deleted file mode 100644 index c4406aa75..000000000 --- a/changelog.d/20261025-v4-pr4c-ii-remove-mpd.md +++ /dev/null @@ -1,57 +0,0 @@ -### Removed -- **The `MultiPeriodDiD` class, its `MultiPeriodDiDResults` container, the `PeriodEffect` - record and the `EventStudy` alias are gone** (`M-010`, `M-011`, `M-012`, `M-060`; 4.0 cut, - PR 4c-ii). `from diff_diff import MultiPeriodDiD` (and the other three names) raises - `ImportError`. The estimator is `TwoWayFixedEffects().fit(..., event_study=True, spec=, - post_periods=, reference_period=)`, which returns the unified `EventStudyResults` surface - natively: `spec="pooled"` is the removed class's design (bit-exact without `unit=`; with - `unit=` the pooled spec auto-clusters at unit), `spec="within"` is its `absorb=["unit"]` - design; `post_periods=` is required (the midpoint default died with the class) and the - post-period average `avg_att` has no successor field. Pinned field for field against the - pre-refactor capture `tests/fixtures/v4_twfe_event_study_oracles.json`. -- The `pyproject.toml` pytest `filterwarnings` entry that silenced the removed class's - construction `FutureWarning`, the `MultiPeriodDiD` power-simulation profile - (`simulate_power` has no event-study profile yet; TODO row), and the `MultiPeriodDiDResults` - branches of DiagnosticReport / BusinessReport / `practitioner_next_steps` (both reports still - reject `EventStudyResults` by design; the admission TODO row records where the logic lived). - -### Breaking Changes -- **`TwoWayFixedEffects.fit(time=)` in static mode now raises `ValueError`** naming `post=` - (`M-083`). Through 3.x `time=` was the deprecated alias for the 0/1 post indicator and - emitted a `FutureWarning` (`M-082`); since 4.0 `time` means the event-study calendar column - only. Supplying both `post=` and `time=` keeps raising the "pass only post=" error. -- **`EventStudyResults.period_effects` is removed** (`M-016`; the compatibility view - this item first shipped was retired at 4.0 by item 9b - no release carried it). Through - the pre-release window, on a calendar surface it returned the former calendar-keyed dict of - per-period records (`period, effect, se, t_stat, p_value, conf_int, is_significant, - significance_stars`, reference period omitted; `is_significant` is evaluated at the fit's - `alpha`, where the removed record hard-coded 0.05; duplicate labels raise). Relative-time - `aggregate("event_study")` surfaces do not carry it (`AttributeError`, no warning). The - successor is `to_dataframe()` and the columnar arrays. -- The 3.x "default reference_period has changed" `FutureWarning` is no longer emitted - (`M-007`): the event-study mode defaults to the last pre-period silently. -- `simulate_power` and friends read the flat `att`/`se`/`p_value`/`conf_int` names from - unregistered custom estimators' results and raise `TypeError` naming `_EstimatorProfile` - otherwise (the `avg_*` fallback left with the removed container; pass `result_extractor=` - for other shapes). - -### Internal -- `DifferenceInDifferences._fit_event_study_core` serves only the TWFE event-study mode - (`post_periods` required by the core; `include_treatment_main` / `cluster_override` - keyword-required); the private payload is pruned to the fields the surface builder reads. - The clustered `hc2_bm` contrast batch keeps the former post-period-average column as the - batch-relative Bell-McCaffrey DOF guard's anchor so per-period NaN masks stay bit-identical - (REGISTRY Note; TODO row for the guard redesign). `build_event_study_surface` opens with an - `isinstance(EventStudyResults)` passthrough. -- Ledger: `M-010`/`M-011`/`M-012` -> `removed`, `M-060`/`M-007`/`M-083` -> `done`, `M-016` - -> `shimmed` on `diff_diff:EventStudyResults.period_effects`, `M-082` keeps `shimmed` with - `warning: null`. Spec item 4c-ii carries the SHIPPED note incl. the three recorded - deviations (anchor column, new live benchmark script, DR/BR branch deletion). -- Tests: the MPD-only tests are deleted or re-fixtured onto `cs_fit` / the TWFE surface - (`tests/test_v4_merge_mpd.py` carries the removal, `M-016` and `M-007` pins and the anchor - pin); the oracle fixture gains two clustered `hc2_bm` lanes captured at `d45d5316` before - the core pruning (its `mpd_twin` records are pruned in PR 4c-ii-b); the clustered - per-period CR2 DOF R/clubSandwich pin and the event-study covariate-collision guard are - migrated to TWFE; variance-conventions `twfe_es_*` rows re-pinned (`tail_df` 5) and the table - regenerated. The methodology/benchmark prose half and the oracle fixture's - `mpd_twin` prune follow in PR 4c-ii-b. diff --git a/changelog.d/20261026-v4-pr5a-ii-docs-readers.md b/changelog.d/20261026-v4-pr5a-ii-docs-readers.md deleted file mode 100644 index 9816fe3b1..000000000 --- a/changelog.d/20261026-v4-pr5a-ii-docs-readers.md +++ /dev/null @@ -1,13 +0,0 @@ -### Internal -- 4.0 cut, PR 5a-ii (the documentation half of cut-sequence item 5's reader migration, - split from PR 5a because the combined diff exceeded the AI review input budget): every - documented read of the 3.x `overall_att` / `overall_se` / `overall_t_stat` / - `overall_p_value` / `overall_conf_int` names (and ContinuousDiD's `overall_att_*` - spellings) on the nine `M-050`..`M-058` containers - RTD pages, the bundled - `llms*.txt` guides, tutorial code cells, the tutorial drift tests that mirror them - (tutorial 32's code-cell hash pins re-pinned), benchmark scripts and the carousel - generator - now reads the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` - quintet. No user-visible behaviour change (the canonical names already resolve); - committed notebook outputs are unchanged except tutorial 18's echoed guidance string. - Bootstrap draw containers (`bootstrap_results.overall_att_se`) and R / golden-JSON - interface keys are untouched. diff --git a/changelog.d/20261026-v4-pr5a-overall-att-readers.md b/changelog.d/20261026-v4-pr5a-overall-att-readers.md deleted file mode 100644 index f26a819d4..000000000 --- a/changelog.d/20261026-v4-pr5a-overall-att-readers.md +++ /dev/null @@ -1,16 +0,0 @@ -### Internal -- 4.0 cut, PR 5a (the reader half of cut-sequence item 5, the `M-050`..`M-058` storage - flip): every in-repo reader of the 3.x `overall_att` / `overall_se` / `overall_t_stat` / - `overall_p_value` / `overall_conf_int` field names (and ContinuousDiD's `overall_att_*` - spellings) on the nine flipping containers in SOURCE and TESTS - consumers, the - containers' own `summary()` / `to_dict()` / `__repr__` / aggregation bodies, and every - test suite - now reads the canonical `att` / `se` / - `t_stat` / `p_value` / `conf_int` quintet. No user-visible change: the canonical names - already resolve as properties, numbers are bit-identical and nothing warns yet; the - storage flip itself, the `FutureWarning` shims and the report-schema changes follow in - PR 5b; the documentation / guides / tutorial / benchmark half of the reader migration - is PR 5a-ii (split off because the combined diff exceeded the AI review input budget). `power.py`'s `_extract_staggered` (a silent `getattr` fallback chain over the old - names) is deleted - the staggered power profiles use `_extract_simple`; the - `practitioner_next_steps` `overall_att` fallback is gone. Bootstrap draw containers - (`CSBootstrapResults.overall_att_se` and siblings) and `StaggeredTripleDiffResults` - (unified at cut item 6) are untouched. diff --git a/changelog.d/20261026-v4-pr5b-storage-flip.md b/changelog.d/20261026-v4-pr5b-storage-flip.md deleted file mode 100644 index 949dfe850..000000000 --- a/changelog.d/20261026-v4-pr5b-storage-flip.md +++ /dev/null @@ -1,8 +0,0 @@ -### Breaking Changes - -- **4.0 cut, storage flip of the headline quintet (`M-050`..`M-058`).** `CallawaySantAnnaResults` (and `DMLDiDResults`), `SunAbrahamResults`, `ImputationDiDResults`, `TwoStageDiDResults`, `StackedDiDResults`, `EfficientDiDResults`, `WooldridgeDiDResults`, `ChaisemartinDHaultfoeuilleResults` and `ContinuousDiDResults` now store the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` as their native dataclass fields (renamed in slot, so the positional constructor layout is unchanged) and take those names as constructor keywords (`overall_att=` raises `TypeError`). The 3.x `overall_att` / `overall_se` / `overall_t_stat` / `overall_p_value` / `overall_conf_int` names - and on `ContinuousDiDResults` all nine legacy spellings, the `overall_att_*` fields and the former silent `overall_se`-style aliases - are removed at 4.0 (item 9b retired the `FutureWarning` property shims this item first shipped - reading one raises `AttributeError`); they are absent from `dataclasses.fields()`, `asdict()` and `to_dict()`. 3.x pickles load unchanged (each container's `__setstate__` rewrites the legacy keys on load). The bootstrap draw containers (`bootstrap_results.overall_att_se` and siblings) are unchanged, and `StaggeredTripleDiffResults` keeps its `overall_*` storage until the DDD container unification. -- **Report schemas.** `DiagnosticReport.to_dict()` reports `headline_metric.name` and `target_parameter.headline_attribute` as `"att"` for the nine containers above (`"overall_att"` under 3.x); `DIAGNOSTIC_REPORT_SCHEMA_VERSION` 2.0 -> 3.0. `BusinessReport.to_dict()` reports `target_parameter.headline_attribute` likewise; `BUSINESS_REPORT_SCHEMA_VERSION` 3.0 -> 4.0 (its `headline` block carries no name key). `StaggeredTripleDiffResults` keeps `headline_attribute: "overall_att"` until item 6. - -### Internal - -- Ledger rows `M-050`..`M-058` flip to `shimmed` with `tests/test_v4_storage_flip.py` as their `test_ref`; `diff_diff._deprecation` gains `LEGACY_OVERALL_QUINTET` and `migrate_renamed_state_keys` (the pickle-migration infrastructure); `DMLDiDResults` re-declares the five shims so the warning names the subclass (naming-guard allowlist reason reworded); `_extract_scalar_headline`'s legacy alias row is deleted; `tests/test_result_aliases.py` pins the post-flip direction (SDDD kept in the 3.x direction); the API autosummary stubs are regenerated; the bootstrap draw containers' payload names are recorded as a REGISTRY `Note` (user decision 2026-10-26); spec item 5 carries its SHIPPED note. diff --git a/changelog.d/20261027-v4-pr6-ddd-container-unification.md b/changelog.d/20261027-v4-pr6-ddd-container-unification.md deleted file mode 100644 index 721546e99..000000000 --- a/changelog.d/20261027-v4-pr6-ddd-container-unification.md +++ /dev/null @@ -1,12 +0,0 @@ -### Breaking Changes - -- **4.0 cut, DDD container unification (`M-014`, `M-140`, `M-141`).** `TripleDifference.fit()` returns ONE `TripleDifferenceResults` for both designs: `results.design` is `"2x2x2"` or `"staggered"`, the canonical `att` / `se` / `t_stat` / `p_value` / `conf_int` are native in both, the 2x2x2 positional layout is unchanged, and the staggered structure (`group_time_effects`, `groups`, `time_periods`, unit counts, `control_group`, `base_period`, `anticipation`, `bootstrap_results`, ...) is appended with defaults (`group_time_effects` is `{}` in the 2x2x2 design). The 3.x `StaggeredTripleDiffResults` class, its module, its `overall_*` fields and `to_dict()` keys, and its fit-time `overall_att_es` / `overall_se_es` / `overall_t_stat_es` / `overall_p_value_es` / `overall_conf_int_es` fields are REMOVED (3.x staggered pickles cannot load; 3.x 2x2x2 pickles load unchanged). The fit-time `aggregate=` / `balance_e=` keywords of `TripleDifference.fit()` are REMOVED (`TypeError`): aggregation is post-fit - `results.aggregate('event_study')` (`EventStudyResults`, `balance_e=`), `results.aggregate('group')` (`AggregationResult`) and `results.aggregate('simple')` (a one-row relay of the stored headline, both designs); bootstrapped fits REPLAY the fit-time multiplier bootstrap so sup-t bands and percentile inference match a 3.x fit-time aggregation; the 2x2x2 design fails the staggered levels closed. The paper's Eq. 4.14 overall is `results.aggregate('simple', weights='event_time')` (accepts `balance_e=`; both 3.x warnings preserved). `summary()` no longer prints the event-study / cohort / Eq. 4.14 blocks; `to_dataframe('event_study' | 'group')` raise with the post-fit pointer; `to_dataframe(level=None)` defaults to `'group_time'` in the staggered design and `'overall'` in 2x2x2 (whose one-row frame gains `design`, `pscore_trim`, `alpha`, `df_inference`). New field `df_inference` records the 2x2x2 engine's t df. `plot_group_effects` and its siblings raise `ValueError` on a 2x2x2 fit (no cells). -- **Report schemas.** For staggered DDD fits `DiagnosticReport.to_dict()['estimator']` and `BusinessReport.to_dict()['estimator']['class_name']` read `TripleDifferenceResults`, and `target_parameter.headline_attribute` reads `"att"` (the last `"overall_att"`). Both schemas gain an additive key - DR a top-level `design`, BR `estimator.design` - `"2x2x2"` / `"staggered"` for `TripleDifferenceResults`, `None` otherwise. No version bump: `DIAGNOSTIC_REPORT_SCHEMA_VERSION` 3.0 and `BUSINESS_REPORT_SCHEMA_VERSION` 4.0 had not shipped (folded into the PR 5b bump). `power.py`'s staggered-boundary guard no longer keys on `aggregate` / `balance_e` (an `estimator_kwargs` carrying them surfaces as `fit()`'s `TypeError`). - -### Fixed - -- **`DiagnosticReport` kept every event-study horizon on fractional calendars.** The parallel-trends and heterogeneity collectors keyed the derived `aggregate('event_study')` surface (and compared raw `event_study_effects` keys) through `int(k)`, so on a half-step calendar (`e = t - g` in `-1.5, -1.0, -0.5, ...`) `-1.5` merged into `-1` and `-0.5` into the treatment horizon `0`: pre-periods silently left the PT family and a violated horizon could read as a clean verdict. Integer-valued horizons keep their `int` keys; fractional horizons stay `float`, compare at their real value against the anticipation-aware boundary, and align with the covariance index for the joint Wald. Affected every post-fit event-study producer routed through the report (CallawaySantAnna included), not only the staggered DDD route this release enables. - -### Internal - -- Ledger rows `M-014`, `M-140`, `M-141` flip to `removed` with `tests/test_v4_unify_ddd.py` as their `test_ref`; `diff_diff/staggered_triple_diff_results.py` is renamed to `diff_diff/triple_diff_results.py` (hosting the unified container, re-exported from `triple_diff.py`); the staggered engine attaches the CallawaySantAnna aggregation kit (`staggered.py`'s `_build_aggregation_kit` on the eligibility-zeroed precomputed copy, plus the `ddd_df_survey_original` bookkeeping key for the Eq. 4.14 df rule); report consumers dispatch on the design through `_reporting_helpers.results_design` / `is_staggered_ddd` (DR's `_DESIGN_APPLICABILITY` / `_DESIGN_PT_METHOD`); the practitioner handler gains a staggered branch; `docs/api/_autosummary` regenerated; the spec's section 4.2 / section 6 / item-6 SHIPPED notes and the REGISTRY `## TripleDifference` post-fit Note record the mechanics and the `weights='event_time'` decision. diff --git a/changelog.d/20261028-v4-pr7a-df-convention-flip.md b/changelog.d/20261028-v4-pr7a-df-convention-flip.md deleted file mode 100644 index f639797b5..000000000 --- a/changelog.d/20261028-v4-pr7a-df-convention-flip.md +++ /dev/null @@ -1,7 +0,0 @@ -### Breaking Changes - -- **4.0 cut, `df_convention` default flip (`M-004`, `M-005`, `M-006`, `M-128`, `M-129`, `M-130`, `M-131`).** The analytical tail-df convention for CLUSTERED fits defaults to `"cluster"` (the Stata/fixest `t(G - 1)` reference) instead of `"residual"` (the fitted residual df `t(n - K_full)`) on `DifferenceInDifferences`, `TwoWayFixedEffects`, `LinearRegression`, `SunAbraham`, `WooldridgeDiD` (OLS arms), `StackedDiD` and `ImputationDiD` (pretrends leads); `LPDiD` already defaulted to `"cluster"` at 3.9. What moves: p-values and confidence intervals on clustered analytical fits - `TwoWayFixedEffects` (static, and event-study with `unit=`), `SunAbraham`, `WooldridgeDiD` OLS and `StackedDiD` cluster at `unit` by default, so their default fits move; `ImputationDiD` moves on the `pretrends=True` lead rows; `DifferenceInDifferences` / `LinearRegression` move only with an explicit `cluster=` / `cluster_ids=`; the analytical lanes inside bootstrapped fits (`WooldridgeDiD` per-cell inference and post-fit `aggregate()`, `ImputationDiD` bootstrapped leads) move too, as do the fake-timing / fake-group placebo diagnostics and the power simulators that relay those p-values. What does not: point estimates, standard errors and t-statistics; survey-design fits (survey df keeps precedence); `vcov_type="hc2_bm"` (Bell-McCaffrey DOF keep precedence); bootstrap percentile p-values / CIs; unclustered and Conley fits; the BJS overall/post inference of `ImputationDiD`; `WooldridgeDiD` logit/poisson arms; permutation and leave-one-out diagnostics. `df_convention="residual"` restores the 3.x inference bit-for-bit; on the surfaces where the knob is a documented no-op (non-`pretrends` `ImputationDiD`, `WooldridgeDiD` logit/poisson) the inert-config `UserWarning` now fires for an explicit `"residual"` rather than for an explicit `"cluster"`. `SyntheticDiD` inherits the flipped attribute from `DifferenceInDifferences` without reading it (no behaviour change). - -### Internal - -- Ledger rows `M-004`..`M-006` and `M-128`..`M-131` flip to `done` with `tests/test_v4_inference_defaults.py` as their `test_ref`; the spec's section 7 / item 7 SHIPPED notes and item 10 now record the deferred bulk re-execution of the nine tutorials whose clustered-default outputs moved (24 re-executed here if its outputs moved); the REGISTRY TwoWayFixedEffects clustered-CR1 inference-df note is relabelled a plain Note (the default now matches fixest) and the SunAbraham / ImputationDiD / StackedDiD / WooldridgeDiD / LPDiD tail-df notes read the 4.0 default; `TestDfConvention`, `test_moderate_t_pins_cluster_df_convention` and the per-estimator knob suites pin the G-1 default against explicit `"residual"` baselines (every default-vs-`"cluster"` comparison became default-vs-`"residual"` so none passes vacuously); `test_ci_matches_r_twfe` tightens to machine precision; the variance-conventions audit matrix keeps its residual-df rows as explicit `"residual"` arms (the K_full accounting pins) and gains eight G-1 `_default_df` twins (three in the fast set), with `docs/methodology/variance-conventions.md` regenerated; the committed TWFE event-study oracle lanes `pooled_cluster_unit` / `within_cluster_unit` and the ImputationDiD `pretrends_all` contract lanes name the `"residual"` convention they were captured under (metadata-only, no oracle number touched); the `DEFERRED.md` Version-gated (v4) row is retired. diff --git a/changelog.d/20261029-v4-pr7b-m080-cluster-policy.md b/changelog.d/20261029-v4-pr7b-m080-cluster-policy.md deleted file mode 100644 index 765610108..000000000 --- a/changelog.d/20261029-v4-pr7b-m080-cluster-policy.md +++ /dev/null @@ -1,10 +0,0 @@ -### Breaking Changes - -- `SpilloverDiD` auto-clusters at `unit` when `cluster=` is omitted ([M-080], item 7b of the 4.0 cut), mirroring `TwoWayFixedEffects`' carve-outs (explicit `cluster=` wins; the auto-cluster is dropped on the Conley path and never injected as a survey PSU). Its default standard errors, p-values and CIs move; `cluster=False` reproduces the 3.x observation-level HC1 numbers. Results report `cluster_name="unit"` / `n_clusters`. Inference stays on the residual df until the `df_convention` knob lands ([M-154], the next PR). -- `cluster=False` is the uniform explicit off-switch ([M-080]): the plain heteroskedasticity-robust sandwich with residual-df t inference on `TwoWayFixedEffects` (static + event-study; one-way CR2 Bell-McCaffrey under `hc2_bm`), `SunAbraham`, `WooldridgeDiD` (OLS within-transform lane with the absorbed-FE full-K rescale; logit/poisson plain QMLE sandwich) and `LPDiD` (per-horizon HC1; `if_robust` unclustered IF variance on the regression-adjustment path; implicit per-observation PSUs under a survey design); an accepted bit-identical no-op on the unit-level-by-construction estimators (`CallawaySantAnna` incl. its RC auto-PSU path, `EfficientDiD` incl. `hausman_pretest`, `DMLDiD`, `ChaisemartinDHaultfoeuille`, staggered `TripleDifference`, `LWDiD` incl. the sensitivity helpers, `HeterogeneousAdoptionDiD`, `ImputationDiD`, `TwoStageDiD`) and on the 2x2 `DifferenceInDifferences` / 2x2x2 `TripleDifference` surfaces; still rejected on `StackedDiD`. Rejected with `inference="wild_bootstrap"` (DiD/TWFE), `WooldridgeDiD(n_bootstrap > 0)` and `vcov_type="hc2_bm"` on `SunAbraham`/`WooldridgeDiD` (cluster-only Bell-McCaffrey DOF; tracked in TODO.md). -- `cluster` accepts `str | None | False` only ([M-155], `diff_diff.utils.validate_cluster`): `True` and non-`str` column labels raise one shared `ValueError` at construction, through `set_params` and at `fit()` on every estimator exposing the parameter (DMLDiD's existing rule generalized; integer-labelled cluster columns must be renamed). -- Cluster metadata: `DifferenceInDifferences` / `TwoWayFixedEffects` report `n_clusters` from the regression's effective cluster count on non-survey analytical fits (previously `None` unless the wild bootstrap ran); `SunAbrahamResults` gains an appended `n_clusters` field and reports `cluster_name` on clustered analytical fits (previously `None` off the Conley path), and its `summary()` renders the variance-family label on analytical non-survey fits; `TwoWayFixedEffects` under a survey design with no explicit `cluster=` reports `cluster_name=None` (the auto-cluster is never injected as a PSU). - -### Internal - -- Ledger: M-080 and the new M-155 (shared cluster contract) -> `done`; new M-154 (SpilloverDiD `df_convention`, item 7c) -> `planned`; pinned by `tests/test_v4_cluster_policy.py`. Tutorial 23 section 6 shows the four-row SE table (CR1 at unit, HC1 via `cluster=False`, Conley lag 0 / lag 1) and was re-executed; `tests/test_t23_spillover_tva_drift.py` pins the new default and the table's source. diff --git a/changelog.d/20261029-v4-pr7c-m154-spillover-df-convention.md b/changelog.d/20261029-v4-pr7c-m154-spillover-df-convention.md deleted file mode 100644 index 661b634ef..000000000 --- a/changelog.d/20261029-v4-pr7c-m154-spillover-df-convention.md +++ /dev/null @@ -1,7 +0,0 @@ -### Breaking Changes - -- `SpilloverDiD` gains the three-value `df_convention` knob with the 4.0 default `"cluster"` ([M-154], item 7c of the 4.0 cut): p-values and confidence intervals on its clustered lanes - the unit auto-cluster and an explicit `cluster=` - now use the Stata/fixest `t(G - 1)` reference like every other auto-clustered default, with `G` the effective cluster count reported as `n_clusters`. Point estimates, standard errors and t-statistics never move. `df_convention="residual"` restores the 3.x residual-df inference bit-for-bit; `"normal"` is deliberate z. `"cluster"` falls back to the residual df on `cluster=False` and on every `vcov_type="conley"` fit, an explicit product-kernel `cluster=` included (`"normal"` still selects z there); a `survey_design` df keeps precedence under every value; a saturated stage-2 sample stays all-NaN under every value. `SpilloverDiDResults` reports `df_convention` and `inference_df` (also in `to_dict()`). - -### Internal - -- Ledger: M-154 -> `done`, pinned by `tests/test_v4_spillover_df_convention.py`; the df ladder is the pure helper `spillover._resolve_inference_df` (survey df -> saturation -> `utils.resolve_tail_df`). The variance-conventions matrix gains the `spillover_cluster_unit` / `spillover_cluster_unit_default_df` rows; the REGISTRY `df_convention` knob roster, `docs/methodology/variance-conventions.md`, the migration guide, `llms-full.txt` and `docs/api/spillover.rst` describe the knob; tutorial 23 re-validated (it prints no p-values or CIs). diff --git a/changelog.d/20261030-v4-pr8a-m093-sentinel-retirement.md b/changelog.d/20261030-v4-pr8a-m093-sentinel-retirement.md deleted file mode 100644 index 9f45e8bc2..000000000 --- a/changelog.d/20261030-v4-pr8a-m093-sentinel-retirement.md +++ /dev/null @@ -1,7 +0,0 @@ -### Breaking Changes - -- Event-study reference rows are marked explicitly ([M-093], item 8a of the 4.0 cut): every native `event_study_effects` dict that materializes its normalization row (`StackedDiD`, `ImputationDiD`, `TwoStageDiD`, `SpilloverDiD`, the `CallawaySantAnna`/`DMLDiD` universal-base aggregation carrier) now carries `"is_reference": True` on that row and NO count key there - the 3.x `n_obs == 0` / `n_groups == 0` sentinel is retired, and every remaining zero count is a genuine count (a non-estimable horizon). `SpilloverDiD`'s event-study `att_dynamic` / `spillover_effects` frames gain a boolean `is_reference` column with `n_obs = NaN` on the reference rows (the column is float; `to_dict()` records follow). The native `to_dataframe(level="event_study")` frames of `StackedDiD` / bootstrapped `ImputationDiD` / `TwoStageDiD` render the reference row's `n_obs` as NaN (float column) until the 8b schema rewire replaces them. `plot_event_study`, `plot_honest_event_study`, HonestDiD, PreTrendsPower and `DiagnosticReport` locate the reference by the key on their raw-dict routes - dispatch, admission, rendering and exception types are unchanged. `TwoStageDiD.summary()` now prints its genuine all-filtered horizons as NaN rows instead of a mislabeled `[ref: h]` line. 3.x pickles are migrated on load. `SunAbraham` (omitted row, `reference_period`) and `EfficientDiD` (PT-Post anchor, an estimated cell named by `reference_period`) are unchanged. - -### Internal - -- `results_base._from_relative_dict` reads only the `is_reference` key and the named `reference_period` (the zero-count heuristic is gone; a keyed row with a finite nonzero effect raises); `_deprecation.migrate_event_study_reference_sentinel` is the single place the retired sentinel is still recognized, run by the carriers' `__setstate__` (`SpilloverDiDResults` gained one; aggregate-mode frames untouched). Ledger: M-093 gains `test_ref` (`tests/test_v4_sentinel_retirement.py`) and stays `planned` until 8b (the `to_dataframe(level="event_study")` schema rewire on every results class, incl. the dCDH native frame's `estimand 'ref'` row). REGISTRY (SpilloverDiD, StackedDiD/ImputationDiD/TwoStageDiD, the plotting Notes), the migration guide, `llms-full.txt` and `docs/api/spillover.rst` describe the marking. diff --git a/changelog.d/20261031-v4-pr8b-m093-event-study-schema.md b/changelog.d/20261031-v4-pr8b-m093-event-study-schema.md deleted file mode 100644 index 4cd641868..000000000 --- a/changelog.d/20261031-v4-pr8b-m093-event-study-schema.md +++ /dev/null @@ -1,7 +0,0 @@ -### Breaking Changes - -- Every results class's `to_dataframe(level="event_study")` now emits the unified `EVENT_STUDY_SCHEMA` ([M-093], item 8b of the 4.0 cut): `event_time, att, se, t_stat, p_value, conf_int_lower, conf_int_upper, cband_lower, cband_upper, n, df, is_reference, estimand` - the frame `results.aggregate("event_study").to_dataframe()` returns (`CallawaySantAnna`/`DMLDiD`, `EfficientDiD`, `ImputationDiD`, `TwoStageDiD`, `StackedDiD`, `ContinuousDiD`, `TripleDifference` staggered, `ChaisemartinDHaultfoeuille`, HAD event-study results, `DurationDiD`, `LWDiD`) or the builder frame (`SunAbraham`, `LPDiD`, `WooldridgeDiD`, `SpilloverDiD`). The 3.x bespoke frames are gone: `relative_period` -> `event_time`; `effect` / `att_glob` / LPDiD's `coefficient` -> `att`; `conf_int_lo`/`hi`, `conf_low`/`high`, HAD's `conf_int_low`/`high` -> `conf_int_lower`/`conf_int_upper`; `n_obs` -> `n` (NaN on the `is_reference` row); dCDH's `horizon` -> `event_time` on the paper's `l1_first_switch` convention, its per-horizon `estimand` labels (`DID^pl_l` / `ref` / `DID_l`) are not carried (`estimand` is `"att"`) and its reference row is marked `is_reference` instead of `n_obs 0`. `WooldridgeDiD` and `LWDiD` append their fit-configuration columns after the schema (`cohort_trends`, `aggregation_weights`; `rolling`, `estimation_method`, `vcov_type`), and `WooldridgeDiD.to_dataframe(level="event_study")` (its default level) now RAISES before `aggregate("event_study", weights=)` instead of returning an empty frame. `CallawaySantAnna`, `EfficientDiD`, `TripleDifference` and analytical `ImputationDiD` / `TwoStageDiD` / `ContinuousDiD` no longer raise "not stored on 4.0 fits" - the level recomputes the surface from the retained kit (bootstrapped `CallawaySantAnna` / `EfficientDiD` / `TripleDifference` fits replay the fit-time bootstrap on every call; an `ImputationDiD` replicate-weight survey fit with `pretrends=True` raises `NotImplementedError` as `aggregate()` does); a kit-less pre-3.9 pickle, or a bootstrapped pickle predating the replay state, that still carries a stored dict now raises where it returned a frame. `level="group"` on those three classes still raises with the post-fit pointer. New levels: `SpilloverDiDResults.to_dataframe(level="simple" | "event_study")` (default `"simple"`, the former one-row frame; aggregate-mode fits raise on `"event_study"`), `HeterogeneousAdoptionDiDEventStudyResults.to_dataframe(level="event_study")`, `DurationDiDResults.to_dataframe(level="event_study")`. An `LPDiD` fit whose stored event-study frame is empty raises instead of returning the empty frame. - -### Internal - -- The routing rule is the M-153 LWDiD precedent (`aggregate(level).to_dataframe()` on the `AggregationMixin` classes, `build_event_study_surface(self).to_dataframe()` elsewhere); `results_base._ABSENT_SURFACE_HINTS` gains a `SunAbrahamResults` entry. Ledger: M-093 flips to `done` with `test_ref` `tests/test_v4_event_study_schema.py` (the cross-producer schema-prefix / route-equality / error-contract pins; `tests/test_v4_sentinel_retirement.py` keeps the 8a pins) and a tenth amendment note; `docs/v4-design.md` section 5 now states the PREFIX rule and item 8 is complete. REGISTRY (CS, EfficientDiD, Imputation/TwoStage/Stacked relay and reference-row Notes, DDD, HAD, plus new to_dataframe-schema Notes for dCDH, SunAbraham, WooldridgeDiD, LPDiD, DurationDiD and SpilloverDiD), the migration guide, `llms-full.txt`, the practitioner guide, `docs/api/wooldridge_etwfe.rst` and `docs/api/spillover.rst` describe the frame; notebooks 16 (re-executed, outputs refreshed and prose reconciled - it leaves the TODO.md tutorial re-execution row), 19 (reads `event_time`/`att`/`is_reference`) and 14 (export prose) updated. TODO.md gains the `level='group'` relay row. diff --git a/changelog.d/20261101-v4-pr9-m090-docs-reorg.md b/changelog.d/20261101-v4-pr9-m090-docs-reorg.md deleted file mode 100644 index 5621302a9..000000000 --- a/changelog.d/20261101-v4-pr9-m090-docs-reorg.md +++ /dev/null @@ -1,9 +0,0 @@ -### Documentation - -- `BaconDecomposition` is re-homed into the diagnostics family ([M-090], item 9 of the 4.0 cut): it leaves the estimator catalogues - README `## Estimators`, `llms.txt` `## Estimators`, the `llms-full.txt` Estimators section and its alias line, the API-reference Estimators autosummary and module toctree, and the docs homepage Supported Estimators table - for their Diagnostics groupings. `RDPlot` moves from the API-reference Visualization autosummary into Diagnostics; `RDPlot`, `RDDensityTest` and the TWFE weight diagnostics leave the `llms-full.txt` Estimators section. Docs-only: the import path, the `Bacon` alias, the results class and every number are unchanged. -- The docs homepage gains a "Diagnostics & Sensitivity" table (BaconDecomposition, RDPlot, RDDensityTest, HonestDiD, PreTrendsPower, PowerAnalysis, DiagnosticReport, the parallel-trends / placebo / TWFE-weight functions and the HAD pretest workflow) next to the estimator table; the API-reference Estimators and Diagnostics intros describe the split. -- `docs/methodology/REGISTRY.md` archive framing for the classes removed at 4.0: the MultiPeriodDiD and StaggeredTripleDifference sections and the QDiD class-spelling subsection open with an archive Note, the table of contents marks them *(archived)* and states the archived-sections convention, the cross-reference tables name `ChangesInChanges(method="qdid")` instead of a standalone `QDiD` estimator, and the "20-estimator count" wording is gone. The migration guide gains the [M-090] bullet. - -### Internal - -- Ledger row M-090 flips to `done` with `tests/test_v4_diagnostic_family_docs.py` as its `test_ref`; the test pins the estimators/diagnostics split on README, `llms.txt`, `llms-full.txt`, `docs/index.rst`, `docs/api/index.rst` and the REGISTRY archive framing, and runs in the docs-tests workflow (README-only diffs now trigger that lane). `docs/doc-deps.yaml` points the diagnostic modules at the new homepage table; the `/docs-check` classification table files Bacon as a diagnostics-class entry. diff --git a/changelog.d/20261101-v4-pr9b-shim-retirement-placebo-time.md b/changelog.d/20261101-v4-pr9b-shim-retirement-placebo-time.md deleted file mode 100644 index 3853818a3..000000000 --- a/changelog.d/20261101-v4-pr9b-shim-retirement-placebo-time.md +++ /dev/null @@ -1,52 +0,0 @@ -### Breaking Changes - -- **4.0 cut, item 9b: the compatibility shims scheduled for a 5.0 removal are removed at 4.0** (user - decision - no 4.x debt; none of these `FutureWarning` shims ever reached a release, so the released - 3.x names break without a warned release - `docs/migration-4.0.md` is the notice). The 3.x - `overall_att` / `overall_se` / `overall_t_stat` / `overall_p_value` / `overall_conf_int` names on - `CallawaySantAnnaResults` (and `DMLDiDResults`), `SunAbrahamResults`, `ImputationDiDResults`, - `TwoStageDiDResults`, `StackedDiDResults`, `EfficientDiDResults`, `WooldridgeDiDResults`, - `ChaisemartinDHaultfoeuilleResults` and - all nine spellings, `overall_att_*` included - - `ContinuousDiDResults` raise `AttributeError`; read the canonical `att` / `se` / `t_stat` / - `p_value` / `conf_int` (`M-050`..`M-058`). 3.x pickles still load (the `__setstate__` key migration - stays). `EventStudyResults.period_effects` is gone; read `to_dataframe()` or the columnar arrays - (`M-016`). `LWDiDResults.pvalue` / `.ci` are gone; read `.p_value` / `.conf_int` (`M-151`, `M-152`). -- **`SyntheticControl(inner_max_iter=, inner_min_decrease=)` are removed** (`M-148`, `M-149`; the - `DeprecationWarning` shipped in 3.13.0): passing either is a `TypeError`, and the positional - parameters after them (`standardize`, `alpha`, `seed`, `v_cv_t0`) move two slots left - pass - them by keyword. -- **`run_placebo_test` / `run_all_placebo_tests`: `time` is the calendar column only and - `post_periods` is required for every `test_type`** (`M-156`, `M-157`; spec section 8 rule 1). The - 0/1 post dummy the permutation and leave-one-out tests need is derived as - `data[time].isin(post_periods)`; a missing `time` column, missing/empty `post_periods`, missing - values in `time`, `post_periods` values absent from the column, or a pre-existing `_placebo_post` - column raise `ValueError` (on the suite wrapper before any test runs). Calls that passed the 0/1 - indicator as `time=` with `post_periods=[1]` return identical numbers; calls that omitted - `post_periods` must add it. - -### Fixed - -- `run_all_placebo_tests` and `run_placebo_test(test_type="permutation" | "leave_one_out")` no - longer fail on a multi-period calendar column: through 3.x the wrappers forwarded `time` itself as - the 0/1 post dummy, so `DifferenceInDifferences.fit` rejected it and the suite silently reported - the two tests as error dicts (tutorial 04's placebo cell printed `ERROR` lines). The dummy is now - derived from `post_periods`. - -### Internal - -- Ledger rows `M-016`, `M-050`..`M-058`, `M-151`, `M-152` flip to `removed` at 4.0 with - `deprecated_in: null` (no release warned); `M-148`/`M-149` flip to `removed` with - `deprecated_in: 3.13.0`; new behavior rows `M-156`/`M-157` (`tests/test_diagnostics.py`); the - ledger header, spec ladder, section 5/8/9/11 notes and the phase-5 roster carry dated amendments. - `diff_diff._deprecation` is reduced to the pickle-migration (`LEGACY_OVERALL_QUINTET`, - `migrate_renamed_state_keys`, `migrate_event_study_reference_sentinel`) and required-arg - (`NOT_SUPPLIED`, `require_arg`) helpers - `deprecated_field_property`, `resolve_renamed_kwarg`, - `warn_deprecated_kwarg` and `deprecated_kwarg_message` are deleted with their last consumers. - With the descriptors gone, assigning a legacy name to a results instance creates a shadow - attribute instead of raising (the non-slotted dataclasses) - the naming guard's consumer sweep - is the in-repo defense. Naming guard: the `DMLDiDResults.overall_att` surface entry and the - `_deprecation.py` `placebo_effects` consumer entry are deleted, the two placebo wrappers join the - rule-1 calendar `time` inventory, and the retired token families' remaining in-repo hits - (body-local `overall_att` names, the kept `__setstate__` key maps, methodology history) are - allowlisted. API autosummary stubs regenerated; TODO.md's placebo-wrapper redesign row closed; - tutorial 17 re-executed (its stored practitioner snippet echoed the old `time='post'` advice). diff --git a/changelog.d/README.md b/changelog.d/README.md index 525857136..85c4fc837 100644 --- a/changelog.d/README.md +++ b/changelog.d/README.md @@ -46,9 +46,10 @@ Rules (validated by `changelog_compile.py check`): never blocks the release. Use inline code instead of fences. - Allowed categories (this list mirrors the compiler constant in `.claude/scripts/changelog_compile.py`; the mirror is test-pinned): - `Added`, `Changed`, `Deprecated`, `Removed`, `Fixed`, `Security`, - `Performance`, `Documentation`, `Testing`, `Breaking Changes`, - `Behavioral Changes`, `Internal`. + `Breaking Changes`, `Behavioral Changes`, `Added`, `Changed`, `Deprecated`, + `Removed`, `Fixed`, `Security`, `Performance`, `Documentation`, `Testing`, + `Internal` (also the compiled section order - the two breaking categories + lead so a major release reads breakage first). - 4.0-program PRs must still name the flipped `M-xxx` row ids in the fragment (the `docs/v4-design.md` per-PR obligation). diff --git a/diff_diff/__init__.py b/diff_diff/__init__.py index 4d9fdb300..566219cd3 100644 --- a/diff_diff/__init__.py +++ b/diff_diff/__init__.py @@ -329,7 +329,7 @@ RDD = RegressionDiscontinuity SCM = SyntheticControl -__version__ = "3.13.0" +__version__ = "4.0.0" __all__ = [ # Estimators "DifferenceInDifferences", diff --git a/diff_diff/diagnostic_report.py b/diff_diff/diagnostic_report.py index c7ac943c0..fcb5c5d6d 100644 --- a/diff_diff/diagnostic_report.py +++ b/diff_diff/diagnostic_report.py @@ -3884,8 +3884,8 @@ def _extract_scalar_headline( (ledger rows M-050..M-058) every scalar-headline estimator container stores the canonical quintet ``att`` / ``se`` / ``p_value`` / ``conf_int`` natively, so the table has a single row; the 3.x - ``overall_*`` / ``overall_att_*`` row was deleted at the cut (reading it - would now emit the containers' FutureWarning shims). The returned + ``overall_*`` / ``overall_att_*`` row was deleted at the cut (reading one of + those names now raises ``AttributeError``; item 9b removed the shims). The returned ``name`` is therefore ``"att"`` for every scalar-headline estimator (the last ``overall_*`` container, ``StaggeredTripleDiffResults``, was retired by the 4.0 DDD container unification, row M-014). diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index e0219940b..c2fb0ba16 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -2,7 +2,7 @@ > A Python library for Difference-in-Differences causal inference analysis. Provides sklearn-like estimators with statsmodels-style output for econometric analysis. -- Version: 3.13.0 +- Version: 4.0.0 - Repository: https://github.com/igerber/diff-diff - License: MIT - Dependencies: numpy, pandas, scipy (no statsmodels dependency) diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 93825c525..7e4e10c51 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -438,7 +438,7 @@ of `docs/v4-design.md`; `source="TwoWayFixedEffects"`, `estimation_spec` and the authoritative `post_periods` partition recorded as provenance). The static 0/1-dummy contract below is unchanged; in event-study mode `time=` is the CALENDAR column (keyword), and the static dummy parameter is `post=` -(row M-082; `time=` remains its deprecated alias through 3.9). +(row M-082; `time=` was its deprecated alias through 3.13 and raises in static mode since 4.0, row M-083). *Designs:* ``` diff --git a/docs/migration-4.0.md b/docs/migration-4.0.md index 31f057ac1..90f17404d 100644 --- a/docs/migration-4.0.md +++ b/docs/migration-4.0.md @@ -1,9 +1,12 @@ # Migrating to 4.0 ```{note} -This describes the **upcoming 4.0 release**. Most of the renames below can be adopted from -3.9 onward, well before 4.0 lands — the new spellings and the old ones both work during the -deprecation window, so you can migrate incrementally and silence the warnings as you go. +This describes the **4.0 release**. 3.9-3.13 shipped `FutureWarning` shims for most of the +renames below, so code migrated during 3.x already runs on 4.0 unchanged. Two caveats: the +surfaces that break without a warned release are marked in their sections (the results fields, +`period_effects`, `LWDiDResults.pvalue`/`.ci`), and the inference defaults that moved +(`df_convention` -> `"cluster"` on seven estimators, SpilloverDiD auto-clustering) change the +NUMBERS of warning-free code - §6 shows how to reproduce 3.x values exactly. ``` diff-diff 4.0 removes the deprecated surface that 3.9 shipped warnings for, merges three pairs @@ -157,8 +160,8 @@ it is the same engine. `method="cic"` is the default and encodes Athey–Imbens' ## Renamed parameters -Most renames are mechanical: the new spelling already exists, so you can adopt it today and the -old one keeps working until 4.0. See the appendix for the full list, and the +Most renames are mechanical: the new spelling has existed since 3.9, and the old one worked +through 3.13 and is gone at 4.0. See the appendix for the full list, and the [R Comparison](r_comparison.rst) page for how these names map onto the R packages. Three families need more than a search-and-replace: @@ -594,8 +597,9 @@ numeric default moved. **Additive API and container changes.** The unified `EventStudyResults` surface, the `AggregationResult` container, and the `Diagnostic` marker base on the diagnostic result roster. -The alias-diet `__getattr__` shim also landed here: `CDiD`, `Stacked` and `Gardner` still import -but now emit a `FutureWarning`, and they no longer appear in `dir()`/`vars()`. +The alias-diet `__getattr__` shim also landed here: `CDiD`, `Stacked` and `Gardner` kept importing +with a `FutureWarning` through 3.13 (hidden from `dir()`/`vars()`) and were retired at 4.0 +([M-132] [M-133] [M-134]). ## Appendix: every 4.0 change diff --git a/docs/troubleshooting.rst b/docs/troubleshooting.rst index 90848327c..8f70c939b 100644 --- a/docs/troubleshooting.rst +++ b/docs/troubleshooting.rst @@ -294,8 +294,8 @@ Performance Issues .. code-block:: python # TWFE already handles unit + time FE via within-transformation - # (post= is the 0/1 post-treatment dummy; the deprecated time= alias - # still works through 3.9) + # (post= is the 0/1 post-treatment dummy; the 3.x time= alias for it + # raises since 4.0 - time= is the event-study calendar column) twfe = TwoWayFixedEffects() results = twfe.fit(data, outcome='y', treatment='treated', unit='unit_id', post='post') diff --git a/docs/tutorials/02_staggered_did.ipynb b/docs/tutorials/02_staggered_did.ipynb index 4c96eb441..d3bd6a397 100644 --- a/docs/tutorials/02_staggered_did.ipynb +++ b/docs/tutorials/02_staggered_did.ipynb @@ -34,10 +34,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.100149Z", - "iopub.status.busy": "2026-10-23T12:10:20.099980Z", - "iopub.status.idle": "2026-10-23T12:10:20.818872Z", - "shell.execute_reply": "2026-10-23T12:10:20.818447Z" + "iopub.execute_input": "2026-11-01T23:34:08.888103Z", + "iopub.status.busy": "2026-11-01T23:34:08.887826Z", + "iopub.status.idle": "2026-11-01T23:34:09.639588Z", + "shell.execute_reply": "2026-11-01T23:34:09.638976Z" } }, "outputs": [], @@ -71,10 +71,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.820346Z", - "iopub.status.busy": "2026-10-23T12:10:20.820253Z", - "iopub.status.idle": "2026-10-23T12:10:20.827086Z", - "shell.execute_reply": "2026-10-23T12:10:20.826656Z" + "iopub.execute_input": "2026-11-01T23:34:09.640809Z", + "iopub.status.busy": "2026-11-01T23:34:09.640716Z", + "iopub.status.idle": "2026-11-01T23:34:09.651852Z", + "shell.execute_reply": "2026-11-01T23:34:09.651473Z" } }, "outputs": [ @@ -269,10 +269,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.842118Z", - "iopub.status.busy": "2026-10-23T12:10:20.842021Z", - "iopub.status.idle": "2026-10-23T12:10:20.845634Z", - "shell.execute_reply": "2026-10-23T12:10:20.845221Z" + "iopub.execute_input": "2026-11-01T23:34:09.666720Z", + "iopub.status.busy": "2026-11-01T23:34:09.666642Z", + "iopub.status.idle": "2026-11-01T23:34:09.672755Z", + "shell.execute_reply": "2026-11-01T23:34:09.672280Z" } }, "outputs": [ @@ -329,10 +329,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.846675Z", - "iopub.status.busy": "2026-10-23T12:10:20.846616Z", - "iopub.status.idle": "2026-10-23T12:10:20.853984Z", - "shell.execute_reply": "2026-10-23T12:10:20.853550Z" + "iopub.execute_input": "2026-11-01T23:34:09.673674Z", + "iopub.status.busy": "2026-11-01T23:34:09.673605Z", + "iopub.status.idle": "2026-11-01T23:34:09.682987Z", + "shell.execute_reply": "2026-11-01T23:34:09.682677Z" } }, "outputs": [ @@ -383,10 +383,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.854936Z", - "iopub.status.busy": "2026-10-23T12:10:20.854873Z", - "iopub.status.idle": "2026-10-23T12:10:20.856519Z", - "shell.execute_reply": "2026-10-23T12:10:20.856160Z" + "iopub.execute_input": "2026-11-01T23:34:09.684022Z", + "iopub.status.busy": "2026-11-01T23:34:09.683961Z", + "iopub.status.idle": "2026-11-01T23:34:09.685696Z", + "shell.execute_reply": "2026-11-01T23:34:09.685341Z" } }, "outputs": [ @@ -445,10 +445,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.857377Z", - "iopub.status.busy": "2026-10-23T12:10:20.857308Z", - "iopub.status.idle": "2026-10-23T12:10:20.991291Z", - "shell.execute_reply": "2026-10-23T12:10:20.990943Z" + "iopub.execute_input": "2026-11-01T23:34:09.686534Z", + "iopub.status.busy": "2026-11-01T23:34:09.686478Z", + "iopub.status.idle": "2026-11-01T23:34:09.825993Z", + "shell.execute_reply": "2026-11-01T23:34:09.825467Z" } }, "outputs": [ @@ -512,10 +512,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:20.992478Z", - "iopub.status.busy": "2026-10-23T12:10:20.992400Z", - "iopub.status.idle": "2026-10-23T12:10:20.999283Z", - "shell.execute_reply": "2026-10-23T12:10:20.998868Z" + "iopub.execute_input": "2026-11-01T23:34:09.827076Z", + "iopub.status.busy": "2026-11-01T23:34:09.827003Z", + "iopub.status.idle": "2026-11-01T23:34:09.835404Z", + "shell.execute_reply": "2026-11-01T23:34:09.834987Z" } }, "outputs": [ @@ -614,10 +614,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.000332Z", - "iopub.status.busy": "2026-10-23T12:10:21.000263Z", - "iopub.status.idle": "2026-10-23T12:10:21.002123Z", - "shell.execute_reply": "2026-10-23T12:10:21.001769Z" + "iopub.execute_input": "2026-11-01T23:34:09.836468Z", + "iopub.status.busy": "2026-11-01T23:34:09.836383Z", + "iopub.status.idle": "2026-11-01T23:34:09.838344Z", + "shell.execute_reply": "2026-11-01T23:34:09.837932Z" } }, "outputs": [ @@ -660,10 +660,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.002937Z", - "iopub.status.busy": "2026-10-23T12:10:21.002880Z", - "iopub.status.idle": "2026-10-23T12:10:21.006476Z", - "shell.execute_reply": "2026-10-23T12:10:21.006119Z" + "iopub.execute_input": "2026-11-01T23:34:09.839176Z", + "iopub.status.busy": "2026-11-01T23:34:09.839111Z", + "iopub.status.idle": "2026-11-01T23:34:09.842837Z", + "shell.execute_reply": "2026-11-01T23:34:09.842516Z" } }, "outputs": [ @@ -940,10 +940,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.007423Z", - "iopub.status.busy": "2026-10-23T12:10:21.007364Z", - "iopub.status.idle": "2026-10-23T12:10:21.009353Z", - "shell.execute_reply": "2026-10-23T12:10:21.008934Z" + "iopub.execute_input": "2026-11-01T23:34:09.843892Z", + "iopub.status.busy": "2026-11-01T23:34:09.843836Z", + "iopub.status.idle": "2026-11-01T23:34:09.845695Z", + "shell.execute_reply": "2026-11-01T23:34:09.845329Z" } }, "outputs": [ @@ -972,10 +972,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.010181Z", - "iopub.status.busy": "2026-10-23T12:10:21.010122Z", - "iopub.status.idle": "2026-10-23T12:10:21.012196Z", - "shell.execute_reply": "2026-10-23T12:10:21.011845Z" + "iopub.execute_input": "2026-11-01T23:34:09.846591Z", + "iopub.status.busy": "2026-11-01T23:34:09.846537Z", + "iopub.status.idle": "2026-11-01T23:34:09.848410Z", + "shell.execute_reply": "2026-11-01T23:34:09.848115Z" } }, "outputs": [ @@ -1003,10 +1003,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.013069Z", - "iopub.status.busy": "2026-10-23T12:10:21.013002Z", - "iopub.status.idle": "2026-10-23T12:10:21.015554Z", - "shell.execute_reply": "2026-10-23T12:10:21.015271Z" + "iopub.execute_input": "2026-11-01T23:34:09.849356Z", + "iopub.status.busy": "2026-11-01T23:34:09.849291Z", + "iopub.status.idle": "2026-11-01T23:34:09.851874Z", + "shell.execute_reply": "2026-11-01T23:34:09.851541Z" } }, "outputs": [ @@ -1070,10 +1070,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.016654Z", - "iopub.status.busy": "2026-10-23T12:10:21.016589Z", - "iopub.status.idle": "2026-10-23T12:10:21.022266Z", - "shell.execute_reply": "2026-10-23T12:10:21.021938Z" + "iopub.execute_input": "2026-11-01T23:34:09.852779Z", + "iopub.status.busy": "2026-11-01T23:34:09.852711Z", + "iopub.status.idle": "2026-11-01T23:34:09.859322Z", + "shell.execute_reply": "2026-11-01T23:34:09.858953Z" } }, "outputs": [ @@ -1123,10 +1123,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.023208Z", - "iopub.status.busy": "2026-10-23T12:10:21.023153Z", - "iopub.status.idle": "2026-10-23T12:10:21.027248Z", - "shell.execute_reply": "2026-10-23T12:10:21.026971Z" + "iopub.execute_input": "2026-11-01T23:34:09.860298Z", + "iopub.status.busy": "2026-11-01T23:34:09.860237Z", + "iopub.status.idle": "2026-11-01T23:34:09.864456Z", + "shell.execute_reply": "2026-11-01T23:34:09.864089Z" } }, "outputs": [ @@ -1185,10 +1185,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.028278Z", - "iopub.status.busy": "2026-10-23T12:10:21.028224Z", - "iopub.status.idle": "2026-10-23T12:10:21.074306Z", - "shell.execute_reply": "2026-10-23T12:10:21.073943Z" + "iopub.execute_input": "2026-11-01T23:34:09.865462Z", + "iopub.status.busy": "2026-11-01T23:34:09.865405Z", + "iopub.status.idle": "2026-11-01T23:34:09.908024Z", + "shell.execute_reply": "2026-11-01T23:34:09.907697Z" } }, "outputs": [ @@ -1234,10 +1234,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.075490Z", - "iopub.status.busy": "2026-10-23T12:10:21.075400Z", - "iopub.status.idle": "2026-10-23T12:10:21.123113Z", - "shell.execute_reply": "2026-10-23T12:10:21.122788Z" + "iopub.execute_input": "2026-11-01T23:34:09.909156Z", + "iopub.status.busy": "2026-11-01T23:34:09.909090Z", + "iopub.status.idle": "2026-11-01T23:34:09.954617Z", + "shell.execute_reply": "2026-11-01T23:34:09.954259Z" } }, "outputs": [ @@ -1294,10 +1294,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.124394Z", - "iopub.status.busy": "2026-10-23T12:10:21.124311Z", - "iopub.status.idle": "2026-10-23T12:10:21.129580Z", - "shell.execute_reply": "2026-10-23T12:10:21.129229Z" + "iopub.execute_input": "2026-11-01T23:34:09.955697Z", + "iopub.status.busy": "2026-11-01T23:34:09.955619Z", + "iopub.status.idle": "2026-11-01T23:34:09.960889Z", + "shell.execute_reply": "2026-11-01T23:34:09.960453Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.130555Z", - "iopub.status.busy": "2026-10-23T12:10:21.130500Z", - "iopub.status.idle": "2026-10-23T12:10:21.133357Z", - "shell.execute_reply": "2026-10-23T12:10:21.133012Z" + "iopub.execute_input": "2026-11-01T23:34:09.961758Z", + "iopub.status.busy": "2026-11-01T23:34:09.961701Z", + "iopub.status.idle": "2026-11-01T23:34:09.964936Z", + "shell.execute_reply": "2026-11-01T23:34:09.964493Z" } }, "outputs": [ @@ -1397,10 +1397,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.134355Z", - "iopub.status.busy": "2026-10-23T12:10:21.134302Z", - "iopub.status.idle": "2026-10-23T12:10:21.140207Z", - "shell.execute_reply": "2026-10-23T12:10:21.139853Z" + "iopub.execute_input": "2026-11-01T23:34:09.965859Z", + "iopub.status.busy": "2026-11-01T23:34:09.965794Z", + "iopub.status.idle": "2026-11-01T23:34:09.971314Z", + "shell.execute_reply": "2026-11-01T23:34:09.970962Z" } }, "outputs": [ @@ -1501,10 +1501,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.141297Z", - "iopub.status.busy": "2026-10-23T12:10:21.141225Z", - "iopub.status.idle": "2026-10-23T12:10:21.145996Z", - "shell.execute_reply": "2026-10-23T12:10:21.145616Z" + "iopub.execute_input": "2026-11-01T23:34:09.972368Z", + "iopub.status.busy": "2026-11-01T23:34:09.972307Z", + "iopub.status.idle": "2026-11-01T23:34:09.977025Z", + "shell.execute_reply": "2026-11-01T23:34:09.976696Z" } }, "outputs": [ @@ -1556,10 +1556,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.146872Z", - "iopub.status.busy": "2026-10-23T12:10:21.146818Z", - "iopub.status.idle": "2026-10-23T12:10:21.151290Z", - "shell.execute_reply": "2026-10-23T12:10:21.150904Z" + "iopub.execute_input": "2026-11-01T23:34:09.978048Z", + "iopub.status.busy": "2026-11-01T23:34:09.977994Z", + "iopub.status.idle": "2026-11-01T23:34:09.982404Z", + "shell.execute_reply": "2026-11-01T23:34:09.982019Z" } }, "outputs": [ @@ -1603,10 +1603,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.152137Z", - "iopub.status.busy": "2026-10-23T12:10:21.152080Z", - "iopub.status.idle": "2026-10-23T12:10:21.161839Z", - "shell.execute_reply": "2026-10-23T12:10:21.161509Z" + "iopub.execute_input": "2026-11-01T23:34:09.983279Z", + "iopub.status.busy": "2026-11-01T23:34:09.983222Z", + "iopub.status.idle": "2026-11-01T23:34:09.993763Z", + "shell.execute_reply": "2026-11-01T23:34:09.993413Z" } }, "outputs": [ @@ -1659,10 +1659,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.162847Z", - "iopub.status.busy": "2026-10-23T12:10:21.162791Z", - "iopub.status.idle": "2026-10-23T12:10:21.171841Z", - "shell.execute_reply": "2026-10-23T12:10:21.171489Z" + "iopub.execute_input": "2026-11-01T23:34:09.994801Z", + "iopub.status.busy": "2026-11-01T23:34:09.994749Z", + "iopub.status.idle": "2026-11-01T23:34:10.004690Z", + "shell.execute_reply": "2026-11-01T23:34:10.004383Z" } }, "outputs": [ @@ -1677,14 +1677,14 @@ "source: TwoWayFixedEffects time scale: calendar\n", "------------------------------------------------------------------------------\n", " Event time ATT SE t P>|t| [95% CI]\n", - " 0 0.0853 0.2635 0.324 0.746 [ -0.4320, 0.6025]\n", - " 1 -0.1753 0.3108 -0.564 0.573 [ -0.7856, 0.4350]\n", - " 2 -0.4500 0.2343 -1.921 0.055 [ -0.9099, 0.0100]\n", + " 0 0.0853 0.2635 0.324 0.747 [ -0.4375, 0.6080]\n", + " 1 -0.1753 0.3108 -0.564 0.574 [ -0.7921, 0.4415]\n", + " 2 -0.4500 0.2343 -1.921 0.058 [ -0.9148, 0.0148]\n", " 3 0.0000 (reference)\n", - " 4 2.5355 0.2703 9.380 0.000 [ 2.0048, 3.0663]\n", - " 5 2.2599 0.2942 7.681 0.000 [ 1.6822, 2.8376]\n", - " 6 2.6983 0.3141 8.589 0.000 [ 2.0815, 3.3151]\n", - " 7 2.7879 0.3045 9.155 0.000 [ 2.1900, 3.3858]\n", + " 4 2.5355 0.2703 9.380 0.000 [ 1.9992, 3.0719]\n", + " 5 2.2599 0.2942 7.681 0.000 [ 1.6761, 2.8437]\n", + " 6 2.6983 0.3141 8.589 0.000 [ 2.0750, 3.3217]\n", + " 7 2.7879 0.3045 9.155 0.000 [ 2.1837, 3.3922]\n", "==============================================================================\n" ] } @@ -1726,10 +1726,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.172732Z", - "iopub.status.busy": "2026-10-23T12:10:21.172675Z", - "iopub.status.idle": "2026-10-23T12:10:21.175312Z", - "shell.execute_reply": "2026-10-23T12:10:21.174979Z" + "iopub.execute_input": "2026-11-01T23:34:10.005743Z", + "iopub.status.busy": "2026-11-01T23:34:10.005690Z", + "iopub.status.idle": "2026-11-01T23:34:10.008576Z", + "shell.execute_reply": "2026-11-01T23:34:10.008191Z" } }, "outputs": [ @@ -1740,9 +1740,9 @@ "\n", "Period-specific effects:\n", " event_time att se p_value\n", - " 0 0.0853 0.2635 0.7463\n", - " 1 -0.1753 0.3108 0.5729\n", - " 2 -0.4500 0.2343 0.0552\n", + " 0 0.0853 0.2635 0.7469\n", + " 1 -0.1753 0.3108 0.5740\n", + " 2 -0.4500 0.2343 0.0576\n", " 4 2.5355 0.2703 0.0000\n", " 5 2.2599 0.2942 0.0000\n", " 6 2.6983 0.3141 0.0000\n", @@ -1785,10 +1785,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.176302Z", - "iopub.status.busy": "2026-10-23T12:10:21.176248Z", - "iopub.status.idle": "2026-10-23T12:10:21.185426Z", - "shell.execute_reply": "2026-10-23T12:10:21.185074Z" + "iopub.execute_input": "2026-11-01T23:34:10.009589Z", + "iopub.status.busy": "2026-11-01T23:34:10.009527Z", + "iopub.status.idle": "2026-11-01T23:34:10.021891Z", + "shell.execute_reply": "2026-11-01T23:34:10.021499Z" } }, "outputs": [ @@ -1807,6 +1807,8 @@ "Time periods: 8\n", "Control group: never_treated\n", "\n", + "Std. errors: CR1 cluster-robust at unit, G=100\n", + "\n", "-------------------------------------------------------------------------------------\n", " Overall Average Treatment Effect on the Treated \n", "-------------------------------------------------------------------------------------\n", @@ -1815,7 +1817,7 @@ "ATT 3.4763 0.0918 37.872 0.0000 ***\n", "-------------------------------------------------------------------------------------\n", "\n", - "95% Confidence Interval: [3.2961, 3.6566]\n", + "95% Confidence Interval: [3.2942, 3.6585]\n", "CV (SE/abs(ATT)): 0.0264\n", "\n", "-------------------------------------------------------------------------------------\n", @@ -1823,10 +1825,10 @@ "-------------------------------------------------------------------------------------\n", "Rel. Period Estimate Std. Err. t-stat P>|t| Sig.\n", "-------------------------------------------------------------------------------------\n", - "-5 -0.3391 0.1755 -1.932 0.0537 .\n", - "-4 -0.3913 0.1656 -2.363 0.0184 *\n", - "-3 -0.2356 0.1073 -2.196 0.0284 *\n", - "-2 -0.1720 0.1265 -1.360 0.1744 \n", + "-5 -0.3391 0.1755 -1.932 0.0562 .\n", + "-4 -0.3913 0.1656 -2.363 0.0201 *\n", + "-3 -0.2356 0.1073 -2.196 0.0304 *\n", + "-2 -0.1720 0.1265 -1.360 0.1770 \n", "0 1.8625 0.1082 17.213 0.0000 ***\n", "1 2.8985 0.0996 29.102 0.0000 ***\n", "2 4.0353 0.1178 34.244 0.0000 ***\n", @@ -1863,10 +1865,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.186398Z", - "iopub.status.busy": "2026-10-23T12:10:21.186338Z", - "iopub.status.idle": "2026-10-23T12:10:21.188778Z", - "shell.execute_reply": "2026-10-23T12:10:21.188460Z" + "iopub.execute_input": "2026-11-01T23:34:10.022797Z", + "iopub.status.busy": "2026-11-01T23:34:10.022742Z", + "iopub.status.idle": "2026-11-01T23:34:10.025183Z", + "shell.execute_reply": "2026-11-01T23:34:10.024797Z" } }, "outputs": [ @@ -1877,10 +1879,10 @@ "Sun-Abraham Event Study Effects:\n", " Rel. Time Effect SE p-value\n", "---------------------------------------------\n", - " -5 -0.3391 0.1755 0.0537 \n", - " -4 -0.3913 0.1656 0.0184 *\n", - " -3 -0.2356 0.1073 0.0284 *\n", - " -2 -0.1720 0.1265 0.1744 \n", + " -5 -0.3391 0.1755 0.0562 \n", + " -4 -0.3913 0.1656 0.0201 *\n", + " -3 -0.2356 0.1073 0.0304 *\n", + " -2 -0.1720 0.1265 0.1770 \n", " 0 1.8625 0.1082 0.0000 *\n", " 1 2.8985 0.0996 0.0000 *\n", " 2 4.0353 0.1178 0.0000 *\n", @@ -1957,10 +1959,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:10:21.189853Z", - "iopub.status.busy": "2026-10-23T12:10:21.189800Z", - "iopub.status.idle": "2026-10-23T12:10:21.196308Z", - "shell.execute_reply": "2026-10-23T12:10:21.196055Z" + "iopub.execute_input": "2026-11-01T23:34:10.026157Z", + "iopub.status.busy": "2026-11-01T23:34:10.026104Z", + "iopub.status.idle": "2026-11-01T23:34:10.032339Z", + "shell.execute_reply": "2026-11-01T23:34:10.032076Z" } }, "outputs": [ @@ -1973,13 +1975,7 @@ "Estimator Overall ATT SE\n", "------------------------------------------------------------\n", "Callaway-Sant'Anna (varying) 3.4763 0.1164\n", - "Sun-Abraham 3.4763 0.0918\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Sun-Abraham 3.4763 0.0918\n", "\n", "\n", "Event Study Comparison:\n", diff --git a/docs/tutorials/09_real_world_examples.ipynb b/docs/tutorials/09_real_world_examples.ipynb index 807907d5b..899ad2cf3 100644 --- a/docs/tutorials/09_real_world_examples.ipynb +++ b/docs/tutorials/09_real_world_examples.ipynb @@ -24,10 +24,10 @@ "id": "cell-1", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.225846Z", - "iopub.status.busy": "2026-10-23T12:08:09.225220Z", - "iopub.status.idle": "2026-10-23T12:08:09.925078Z", - "shell.execute_reply": "2026-10-23T12:08:09.924635Z" + "iopub.execute_input": "2026-11-01T23:34:11.030714Z", + "iopub.status.busy": "2026-11-01T23:34:11.030476Z", + "iopub.status.idle": "2026-11-01T23:34:11.709852Z", + "shell.execute_reply": "2026-11-01T23:34:11.709388Z" } }, "outputs": [], @@ -65,10 +65,10 @@ "id": "cell-2", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.926574Z", - "iopub.status.busy": "2026-10-23T12:08:09.926482Z", - "iopub.status.idle": "2026-10-23T12:08:09.928670Z", - "shell.execute_reply": "2026-10-23T12:08:09.928335Z" + "iopub.execute_input": "2026-11-01T23:34:11.711229Z", + "iopub.status.busy": "2026-11-01T23:34:11.711138Z", + "iopub.status.idle": "2026-11-01T23:34:11.713294Z", + "shell.execute_reply": "2026-11-01T23:34:11.712987Z" } }, "outputs": [ @@ -125,10 +125,10 @@ "id": "cell-4", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.929912Z", - "iopub.status.busy": "2026-10-23T12:08:09.929839Z", - "iopub.status.idle": "2026-10-23T12:08:09.943052Z", - "shell.execute_reply": "2026-10-23T12:08:09.942729Z" + "iopub.execute_input": "2026-11-01T23:34:11.714392Z", + "iopub.status.busy": "2026-11-01T23:34:11.714326Z", + "iopub.status.idle": "2026-11-01T23:34:11.726359Z", + "shell.execute_reply": "2026-11-01T23:34:11.726057Z" } }, "outputs": [ @@ -286,10 +286,10 @@ "id": "cell-5", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.944206Z", - "iopub.status.busy": "2026-10-23T12:08:09.944152Z", - "iopub.status.idle": "2026-10-23T12:08:09.949008Z", - "shell.execute_reply": "2026-10-23T12:08:09.948619Z" + "iopub.execute_input": "2026-11-01T23:34:11.727414Z", + "iopub.status.busy": "2026-11-01T23:34:11.727356Z", + "iopub.status.idle": "2026-11-01T23:34:11.732684Z", + "shell.execute_reply": "2026-11-01T23:34:11.732315Z" } }, "outputs": [ @@ -423,10 +423,10 @@ "id": "cell-7", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.950051Z", - "iopub.status.busy": "2026-10-23T12:08:09.949996Z", - "iopub.status.idle": "2026-10-23T12:08:09.956229Z", - "shell.execute_reply": "2026-10-23T12:08:09.955862Z" + "iopub.execute_input": "2026-11-01T23:34:11.733641Z", + "iopub.status.busy": "2026-11-01T23:34:11.733584Z", + "iopub.status.idle": "2026-11-01T23:34:11.739653Z", + "shell.execute_reply": "2026-11-01T23:34:11.739237Z" } }, "outputs": [ @@ -577,10 +577,10 @@ "id": "cell-9", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.957275Z", - "iopub.status.busy": "2026-10-23T12:08:09.957219Z", - "iopub.status.idle": "2026-10-23T12:08:09.960430Z", - "shell.execute_reply": "2026-10-23T12:08:09.960078Z" + "iopub.execute_input": "2026-11-01T23:34:11.740624Z", + "iopub.status.busy": "2026-11-01T23:34:11.740568Z", + "iopub.status.idle": "2026-11-01T23:34:11.744080Z", + "shell.execute_reply": "2026-11-01T23:34:11.743496Z" } }, "outputs": [ @@ -636,10 +636,10 @@ "id": "cell-10", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.961463Z", - "iopub.status.busy": "2026-10-23T12:08:09.961403Z", - "iopub.status.idle": "2026-10-23T12:08:09.965085Z", - "shell.execute_reply": "2026-10-23T12:08:09.964721Z" + "iopub.execute_input": "2026-11-01T23:34:11.745038Z", + "iopub.status.busy": "2026-11-01T23:34:11.744975Z", + "iopub.status.idle": "2026-11-01T23:34:11.748633Z", + "shell.execute_reply": "2026-11-01T23:34:11.748167Z" } }, "outputs": [ @@ -689,10 +689,10 @@ "id": "cell-11", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.965979Z", - "iopub.status.busy": "2026-10-23T12:08:09.965911Z", - "iopub.status.idle": "2026-10-23T12:08:09.969099Z", - "shell.execute_reply": "2026-10-23T12:08:09.968753Z" + "iopub.execute_input": "2026-11-01T23:34:11.749517Z", + "iopub.status.busy": "2026-11-01T23:34:11.749449Z", + "iopub.status.idle": "2026-11-01T23:34:11.752664Z", + "shell.execute_reply": "2026-11-01T23:34:11.752302Z" } }, "outputs": [ @@ -764,10 +764,10 @@ "id": "cell-13", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:09.970117Z", - "iopub.status.busy": "2026-10-23T12:08:09.970054Z", - "iopub.status.idle": "2026-10-23T12:08:10.080937Z", - "shell.execute_reply": "2026-10-23T12:08:10.080604Z" + "iopub.execute_input": "2026-11-01T23:34:11.753637Z", + "iopub.status.busy": "2026-11-01T23:34:11.753584Z", + "iopub.status.idle": "2026-11-01T23:34:11.859025Z", + "shell.execute_reply": "2026-11-01T23:34:11.858686Z" } }, "outputs": [ @@ -855,10 +855,10 @@ "id": "cell-15", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.082154Z", - "iopub.status.busy": "2026-10-23T12:08:10.082081Z", - "iopub.status.idle": "2026-10-23T12:08:10.093679Z", - "shell.execute_reply": "2026-10-23T12:08:10.093261Z" + "iopub.execute_input": "2026-11-01T23:34:11.860195Z", + "iopub.status.busy": "2026-11-01T23:34:11.860120Z", + "iopub.status.idle": "2026-11-01T23:34:11.872924Z", + "shell.execute_reply": "2026-11-01T23:34:11.872448Z" } }, "outputs": [ @@ -1009,10 +1009,10 @@ "id": "cell-16", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.094667Z", - "iopub.status.busy": "2026-10-23T12:08:10.094612Z", - "iopub.status.idle": "2026-10-23T12:08:10.097770Z", - "shell.execute_reply": "2026-10-23T12:08:10.097455Z" + "iopub.execute_input": "2026-11-01T23:34:11.873881Z", + "iopub.status.busy": "2026-11-01T23:34:11.873807Z", + "iopub.status.idle": "2026-11-01T23:34:11.877387Z", + "shell.execute_reply": "2026-11-01T23:34:11.876977Z" } }, "outputs": [ @@ -1065,10 +1065,10 @@ "id": "cell-18", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.098876Z", - "iopub.status.busy": "2026-10-23T12:08:10.098825Z", - "iopub.status.idle": "2026-10-23T12:08:10.116787Z", - "shell.execute_reply": "2026-10-23T12:08:10.116400Z" + "iopub.execute_input": "2026-11-01T23:34:11.878397Z", + "iopub.status.busy": "2026-11-01T23:34:11.878337Z", + "iopub.status.idle": "2026-11-01T23:34:11.896016Z", + "shell.execute_reply": "2026-11-01T23:34:11.895673Z" } }, "outputs": [ @@ -1108,10 +1108,10 @@ "id": "cell-19", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.117825Z", - "iopub.status.busy": "2026-10-23T12:08:10.117767Z", - "iopub.status.idle": "2026-10-23T12:08:10.119632Z", - "shell.execute_reply": "2026-10-23T12:08:10.119278Z" + "iopub.execute_input": "2026-11-01T23:34:11.896989Z", + "iopub.status.busy": "2026-11-01T23:34:11.896930Z", + "iopub.status.idle": "2026-11-01T23:34:11.898611Z", + "shell.execute_reply": "2026-11-01T23:34:11.898298Z" } }, "outputs": [ @@ -1169,10 +1169,10 @@ "id": "cell-20", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.120587Z", - "iopub.status.busy": "2026-10-23T12:08:10.120531Z", - "iopub.status.idle": "2026-10-23T12:08:10.241257Z", - "shell.execute_reply": "2026-10-23T12:08:10.240837Z" + "iopub.execute_input": "2026-11-01T23:34:11.899527Z", + "iopub.status.busy": "2026-11-01T23:34:11.899471Z", + "iopub.status.idle": "2026-11-01T23:34:12.015647Z", + "shell.execute_reply": "2026-11-01T23:34:12.015298Z" } }, "outputs": [ @@ -1229,10 +1229,10 @@ "id": "cell-22", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.242381Z", - "iopub.status.busy": "2026-10-23T12:08:10.242320Z", - "iopub.status.idle": "2026-10-23T12:08:10.250679Z", - "shell.execute_reply": "2026-10-23T12:08:10.250336Z" + "iopub.execute_input": "2026-11-01T23:34:12.016837Z", + "iopub.status.busy": "2026-11-01T23:34:12.016768Z", + "iopub.status.idle": "2026-11-01T23:34:12.024860Z", + "shell.execute_reply": "2026-11-01T23:34:12.024536Z" } }, "outputs": [ @@ -1293,10 +1293,10 @@ "id": "cell-23", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.251777Z", - "iopub.status.busy": "2026-10-23T12:08:10.251712Z", - "iopub.status.idle": "2026-10-23T12:08:10.256043Z", - "shell.execute_reply": "2026-10-23T12:08:10.255595Z" + "iopub.execute_input": "2026-11-01T23:34:12.025944Z", + "iopub.status.busy": "2026-11-01T23:34:12.025886Z", + "iopub.status.idle": "2026-11-01T23:34:12.030247Z", + "shell.execute_reply": "2026-11-01T23:34:12.029875Z" } }, "outputs": [ @@ -1342,10 +1342,10 @@ "id": "cell-24", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.256977Z", - "iopub.status.busy": "2026-10-23T12:08:10.256918Z", - "iopub.status.idle": "2026-10-23T12:08:10.262000Z", - "shell.execute_reply": "2026-10-23T12:08:10.261659Z" + "iopub.execute_input": "2026-11-01T23:34:12.031119Z", + "iopub.status.busy": "2026-11-01T23:34:12.031054Z", + "iopub.status.idle": "2026-11-01T23:34:12.035978Z", + "shell.execute_reply": "2026-11-01T23:34:12.035649Z" } }, "outputs": [ @@ -1397,10 +1397,10 @@ "id": "cell-25", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.263044Z", - "iopub.status.busy": "2026-10-23T12:08:10.262987Z", - "iopub.status.idle": "2026-10-23T12:08:10.323097Z", - "shell.execute_reply": "2026-10-23T12:08:10.322754Z" + "iopub.execute_input": "2026-11-01T23:34:12.036920Z", + "iopub.status.busy": "2026-11-01T23:34:12.036865Z", + "iopub.status.idle": "2026-11-01T23:34:12.093355Z", + "shell.execute_reply": "2026-11-01T23:34:12.093013Z" } }, "outputs": [ @@ -1455,10 +1455,10 @@ "id": "cell-27", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.324392Z", - "iopub.status.busy": "2026-10-23T12:08:10.324310Z", - "iopub.status.idle": "2026-10-23T12:08:10.343429Z", - "shell.execute_reply": "2026-10-23T12:08:10.343030Z" + "iopub.execute_input": "2026-11-01T23:34:12.094517Z", + "iopub.status.busy": "2026-11-01T23:34:12.094445Z", + "iopub.status.idle": "2026-11-01T23:34:12.112385Z", + "shell.execute_reply": "2026-11-01T23:34:12.112021Z" } }, "outputs": [ @@ -1477,15 +1477,17 @@ "Time periods: 11\n", "Control group: never_treated\n", "\n", + "Std. errors: CR1 cluster-robust at state, G=50\n", + "\n", "-------------------------------------------------------------------------------------\n", " Overall Average Treatment Effect on the Treated \n", "-------------------------------------------------------------------------------------\n", "Parameter Estimate Std. Err. t-stat P>|t| Sig.\n", "-------------------------------------------------------------------------------------\n", - "ATT 0.4725 0.2100 2.250 0.0250 *\n", + "ATT 0.4725 0.2100 2.250 0.0290 *\n", "-------------------------------------------------------------------------------------\n", "\n", - "95% Confidence Interval: [0.0597, 0.8853]\n", + "95% Confidence Interval: [0.0504, 0.8946]\n", "CV (SE/abs(ATT)): 0.4445\n", "\n", "-------------------------------------------------------------------------------------\n", @@ -1494,19 +1496,19 @@ "Rel. Period Estimate Std. Err. t-stat P>|t| Sig.\n", "-------------------------------------------------------------------------------------\n", "-9 -1.1818 0.1920 -6.154 0.0000 ***\n", - "-8 -0.3784 0.1950 -1.940 0.0530 .\n", - "-7 -0.3719 0.2210 -1.683 0.0930 .\n", - "-6 0.5619 0.2868 1.959 0.0507 .\n", - "-5 0.5564 0.2215 2.512 0.0124 *\n", - "-4 0.1663 0.2588 0.643 0.5207 \n", - "-3 0.4702 0.2430 1.935 0.0537 .\n", - "-2 0.3231 0.2343 1.379 0.1686 \n", - "0 0.4655 0.1672 2.783 0.0056 **\n", - "1 0.7781 0.2517 3.091 0.0021 **\n", - "2 0.4840 0.2524 1.918 0.0558 .\n", - "3 0.3673 0.3024 1.215 0.2252 \n", - "4 0.1541 0.3044 0.506 0.6131 \n", - "5 0.3210 0.1891 1.697 0.0903 .\n", + "-8 -0.3784 0.1950 -1.940 0.0581 .\n", + "-7 -0.3719 0.2210 -1.683 0.0987 .\n", + "-6 0.5619 0.2868 1.959 0.0558 .\n", + "-5 0.5564 0.2215 2.512 0.0154 *\n", + "-4 0.1663 0.2588 0.643 0.5234 \n", + "-3 0.4702 0.2430 1.935 0.0588 .\n", + "-2 0.3231 0.2343 1.379 0.1741 \n", + "0 0.4655 0.1672 2.783 0.0076 **\n", + "1 0.7781 0.2517 3.091 0.0033 **\n", + "2 0.4840 0.2524 1.918 0.0609 .\n", + "3 0.3673 0.3024 1.215 0.2304 \n", + "4 0.1541 0.3044 0.506 0.6151 \n", + "5 0.3210 0.1891 1.697 0.0960 .\n", "-------------------------------------------------------------------------------------\n", "\n", "Signif. codes: '***' 0.001, '**' 0.01, '*' 0.05, '.' 0.1\n", @@ -1535,10 +1537,10 @@ "id": "cell-28", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.344517Z", - "iopub.status.busy": "2026-10-23T12:08:10.344455Z", - "iopub.status.idle": "2026-10-23T12:08:10.346517Z", - "shell.execute_reply": "2026-10-23T12:08:10.346237Z" + "iopub.execute_input": "2026-11-01T23:34:12.113439Z", + "iopub.status.busy": "2026-11-01T23:34:12.113382Z", + "iopub.status.idle": "2026-11-01T23:34:12.115616Z", + "shell.execute_reply": "2026-11-01T23:34:12.115331Z" } }, "outputs": [ @@ -1604,10 +1606,10 @@ "id": "cell-30", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.347572Z", - "iopub.status.busy": "2026-10-23T12:08:10.347512Z", - "iopub.status.idle": "2026-10-23T12:08:10.354805Z", - "shell.execute_reply": "2026-10-23T12:08:10.354431Z" + "iopub.execute_input": "2026-11-01T23:34:12.116609Z", + "iopub.status.busy": "2026-11-01T23:34:12.116555Z", + "iopub.status.idle": "2026-11-01T23:34:12.123881Z", + "shell.execute_reply": "2026-11-01T23:34:12.123542Z" } }, "outputs": [ @@ -1625,7 +1627,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_83507/1542907809.py:2: UserWarning: divorce_laws canonical data are unavailable (no verified canonical source is configured); returning a SYNTHETIC fallback. Check `df.attrs['source']` before treating the result as replication data.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_42751/1542907809.py:2: UserWarning: divorce_laws canonical data are unavailable (no verified canonical source is configured); returning a SYNTHETIC fallback. Check `df.attrs['source']` before treating the result as replication data.\n", " divorce = load_divorce_laws()\n" ] }, @@ -1758,10 +1760,10 @@ "id": "cell-31", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.355757Z", - "iopub.status.busy": "2026-10-23T12:08:10.355689Z", - "iopub.status.idle": "2026-10-23T12:08:10.358430Z", - "shell.execute_reply": "2026-10-23T12:08:10.358157Z" + "iopub.execute_input": "2026-11-01T23:34:12.124862Z", + "iopub.status.busy": "2026-11-01T23:34:12.124786Z", + "iopub.status.idle": "2026-11-01T23:34:12.127634Z", + "shell.execute_reply": "2026-11-01T23:34:12.127312Z" } }, "outputs": [ @@ -1810,10 +1812,10 @@ "id": "cell-32", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.359484Z", - "iopub.status.busy": "2026-10-23T12:08:10.359425Z", - "iopub.status.idle": "2026-10-23T12:08:10.373289Z", - "shell.execute_reply": "2026-10-23T12:08:10.372983Z" + "iopub.execute_input": "2026-11-01T23:34:12.128590Z", + "iopub.status.busy": "2026-11-01T23:34:12.128516Z", + "iopub.status.idle": "2026-11-01T23:34:12.141888Z", + "shell.execute_reply": "2026-11-01T23:34:12.141496Z" } }, "outputs": [ @@ -1852,7 +1854,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_83507/2217541306.py:8: UserWarning: 21 (group, time) cell(s) could not be estimated: 21 due to insufficient data or non-estimable cells.\n", + "/var/folders/bh/mzf05nq92hs6t7vn2ssvfhpr0000gn/T/ipykernel_42751/2217541306.py:8: UserWarning: 21 (group, time) cell(s) could not be estimated: 21 due to insufficient data or non-estimable cells.\n", " results_divorce = cs_divorce.fit(\n" ] } @@ -1882,10 +1884,10 @@ "id": "cell-33", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.374346Z", - "iopub.status.busy": "2026-10-23T12:08:10.374291Z", - "iopub.status.idle": "2026-10-23T12:08:10.382283Z", - "shell.execute_reply": "2026-10-23T12:08:10.381994Z" + "iopub.execute_input": "2026-11-01T23:34:12.142840Z", + "iopub.status.busy": "2026-11-01T23:34:12.142771Z", + "iopub.status.idle": "2026-11-01T23:34:12.150234Z", + "shell.execute_reply": "2026-11-01T23:34:12.149837Z" } }, "outputs": [ @@ -1961,10 +1963,10 @@ "id": "cell-34", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.383286Z", - "iopub.status.busy": "2026-10-23T12:08:10.383223Z", - "iopub.status.idle": "2026-10-23T12:08:10.468997Z", - "shell.execute_reply": "2026-10-23T12:08:10.468669Z" + "iopub.execute_input": "2026-11-01T23:34:12.151192Z", + "iopub.status.busy": "2026-11-01T23:34:12.151137Z", + "iopub.status.idle": "2026-11-01T23:34:12.231662Z", + "shell.execute_reply": "2026-11-01T23:34:12.231257Z" } }, "outputs": [ @@ -2023,10 +2025,10 @@ "id": "cell-36", "metadata": { "execution": { - "iopub.execute_input": "2026-10-23T12:08:10.470284Z", - "iopub.status.busy": "2026-10-23T12:08:10.470208Z", - "iopub.status.idle": "2026-10-23T12:08:10.476167Z", - "shell.execute_reply": "2026-10-23T12:08:10.475747Z" + "iopub.execute_input": "2026-11-01T23:34:12.232763Z", + "iopub.status.busy": "2026-11-01T23:34:12.232683Z", + "iopub.status.idle": "2026-11-01T23:34:12.238545Z", + "shell.execute_reply": "2026-11-01T23:34:12.238135Z" } }, "outputs": [ @@ -2035,7 +2037,13 @@ "output_type": "stream", "text": [ "Effects by Adoption Cohort\n", - "==================================================\n", + "==================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Cohort 1969: 0.0254 (SE: 0.1025) \n", "Cohort 1970: 0.5027 (SE: 0.2376) *\n", "Cohort 1971: 0.1403 (SE: 0.1353) \n", diff --git a/docs/tutorials/10_trop.ipynb b/docs/tutorials/10_trop.ipynb index 216db4441..3b900f0f1 100644 --- a/docs/tutorials/10_trop.ipynb +++ b/docs/tutorials/10_trop.ipynb @@ -624,7 +624,7 @@ "- ATT = mean(\u03c4_it) over treated observations\n", "- Faster (single optimization) with global weights\n", "\n", - "Note: `method='twostep'` is a deprecated alias for `method='local'`, and `method='joint'` is a deprecated alias for `method='global'`. Both will be removed in v3.0." + "Note: the former aliases `method='twostep'` (for `method='local'`) and `method='joint'` (for `method='global'`) have been removed; the canonical names are the only spellings." ], "metadata": {} }, diff --git a/docs/tutorials/18_geo_experiments.ipynb b/docs/tutorials/18_geo_experiments.ipynb index 8b155f859..2ec965d97 100644 --- a/docs/tutorials/18_geo_experiments.ipynb +++ b/docs/tutorials/18_geo_experiments.ipynb @@ -900,7 +900,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/Users/igerber/diff-diff/diff_diff/synthetic_did.py:1110: UserWarning: Frank-Wolfe did not converge on 100 of 100 valid bootstrap draws (variance_method='bootstrap'). SE is still reported from the final iterate of each draw, but non-convergent draws may be noisier; consider relaxing min_decrease or increasing pre-period length if regularization is already moderate.\n", + "diff_diff/synthetic_did.py:1110: UserWarning: Frank-Wolfe did not converge on 100 of 100 valid bootstrap draws (variance_method='bootstrap'). SE is still reported from the final iterate of each draw, but non-convergent draws may be noisier; consider relaxing min_decrease or increasing pre-period length if regularization is already moderate.\n", " se_n, bootstrap_estimates_n = self._bootstrap_se(\n" ] }, diff --git a/docs/tutorials/25_synthetic_control_policy.ipynb b/docs/tutorials/25_synthetic_control_policy.ipynb index a874a1c81..b9f17fa3c 100644 --- a/docs/tutorials/25_synthetic_control_policy.ipynb +++ b/docs/tutorials/25_synthetic_control_policy.ipynb @@ -59,10 +59,10 @@ "id": "9894e9a3", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:04.587402Z", - "iopub.status.busy": "2026-10-06T08:04:04.587049Z", - "iopub.status.idle": "2026-10-06T08:04:06.193289Z", - "shell.execute_reply": "2026-10-06T08:04:06.191999Z" + "iopub.execute_input": "2026-11-01T23:35:45.164570Z", + "iopub.status.busy": "2026-11-01T23:35:45.164021Z", + "iopub.status.idle": "2026-11-01T23:35:45.896083Z", + "shell.execute_reply": "2026-11-01T23:35:45.895730Z" } }, "outputs": [ @@ -254,10 +254,10 @@ "id": "243d4304", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:06.196648Z", - "iopub.status.busy": "2026-10-06T08:04:06.196334Z", - "iopub.status.idle": "2026-10-06T08:04:06.349596Z", - "shell.execute_reply": "2026-10-06T08:04:06.348254Z" + "iopub.execute_input": "2026-11-01T23:35:45.897454Z", + "iopub.status.busy": "2026-11-01T23:35:45.897383Z", + "iopub.status.idle": "2026-11-01T23:35:45.942793Z", + "shell.execute_reply": "2026-11-01T23:35:45.942449Z" } }, "outputs": [ @@ -332,10 +332,10 @@ "id": "8e37cda3", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:06.352951Z", - "iopub.status.busy": "2026-10-06T08:04:06.352592Z", - "iopub.status.idle": "2026-10-06T08:04:06.363421Z", - "shell.execute_reply": "2026-10-06T08:04:06.362168Z" + "iopub.execute_input": "2026-11-01T23:35:45.943974Z", + "iopub.status.busy": "2026-11-01T23:35:45.943910Z", + "iopub.status.idle": "2026-11-01T23:35:45.946702Z", + "shell.execute_reply": "2026-11-01T23:35:45.946321Z" } }, "outputs": [ @@ -427,10 +427,10 @@ "id": "5af70671", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:06.365974Z", - "iopub.status.busy": "2026-10-06T08:04:06.365616Z", - "iopub.status.idle": "2026-10-06T08:04:06.566002Z", - "shell.execute_reply": "2026-10-06T08:04:06.564793Z" + "iopub.execute_input": "2026-11-01T23:35:45.947695Z", + "iopub.status.busy": "2026-11-01T23:35:45.947629Z", + "iopub.status.idle": "2026-11-01T23:35:46.000204Z", + "shell.execute_reply": "2026-11-01T23:35:45.999887Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "id": "b6efc707", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:06.568909Z", - "iopub.status.busy": "2026-10-06T08:04:06.568472Z", - "iopub.status.idle": "2026-10-06T08:04:06.730527Z", - "shell.execute_reply": "2026-10-06T08:04:06.729538Z" + "iopub.execute_input": "2026-11-01T23:35:46.001420Z", + "iopub.status.busy": "2026-11-01T23:35:46.001344Z", + "iopub.status.idle": "2026-11-01T23:35:46.042922Z", + "shell.execute_reply": "2026-11-01T23:35:46.042594Z" } }, "outputs": [ @@ -544,10 +544,10 @@ "id": "a11a84fb", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:06.732871Z", - "iopub.status.busy": "2026-10-06T08:04:06.732620Z", - "iopub.status.idle": "2026-10-06T08:04:08.933615Z", - "shell.execute_reply": "2026-10-06T08:04:08.932517Z" + "iopub.execute_input": "2026-11-01T23:35:46.044090Z", + "iopub.status.busy": "2026-11-01T23:35:46.044021Z", + "iopub.status.idle": "2026-11-01T23:35:46.897424Z", + "shell.execute_reply": "2026-11-01T23:35:46.897086Z" } }, "outputs": [ @@ -573,16 +573,16 @@ "id": "78882a15", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:08.935989Z", - "iopub.status.busy": "2026-10-06T08:04:08.935763Z", - "iopub.status.idle": "2026-10-06T08:04:09.075230Z", - "shell.execute_reply": "2026-10-06T08:04:09.073813Z" + "iopub.execute_input": "2026-11-01T23:35:46.898533Z", + "iopub.status.busy": "2026-11-01T23:35:46.898473Z", + "iopub.status.idle": "2026-11-01T23:35:46.929129Z", + "shell.execute_reply": "2026-11-01T23:35:46.928768Z" } }, "outputs": [ { "data": { - "image/png": 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nnnkmzY7Zed5mpQ9x5uqcc85Jt956azrllFOazr5EsH7uuedymVr52oUoLYgDkZg2sLzPzjvvvHyA21w8zzECW57Ws6NtRUlcTNUYtc/lqUPjOo84iI7ANzPx2vvRj37UotQgarqjNjym0ZxZP7tyW9urvdvUlun1sSMqvT0dFWU+8X4R7w0xQFAuKYna/LvvvjtP5xmPmd3nfna2Pd67YuQ/7o/rkeIAJKa4jIPHmFqz+XTC0f7zzz/focAf04nGmb14b2g+BWyc2Tj77LPzgVCldGVb0KSil/sy15nRbDwxm0xzMSNBLP/Rj3400/XGbCoxpVrMnrD00kvnaThj9oWYQrJ1OzFDym677VZaeOGF8+NiOrbrrruuxfpiSsiYci1mx4hZKuKxMR1d9DvW0Xy2i5ghZquttir16tUrzypRW1ubp2aM2WPKYjq3nXbaKU9XFzNCxPriK2ZfGDt27HS3qyN9bms2nmpsR0f6XJ7dJqbni3ZjOrmYQi/aaL1f2jP1ZllMLxrriSk14+fKM4x09LU2q8/brPShLKYlXHPNNfP+iJ+NWWfi+5jGsyymG4wZUaJfsc/i/hdeeKG0zDLLtJg5Jab7jJmq4nHlmUkaGxs71FbM1PL9738/P4cxA0/sh7POOivvq5nNxhOz9sRrLaYrjekV4zUYv6MxI1PMltKefnbltrZnNp72blNbZtTH8pSYrV188cV5P7/55pstls/q9kyvnZgZpvWsP9ObLSdmpPnZz36Wt3/gwIH5vSJeF82nCK3Ecz89M9v2mNYyZhyKaVpb/1wsj+l9y2KWm+h7vLaj7XjPKov+xpSZbYnnI15/MXtOvCeU3x/idyWmi20+G0/zv0Mzeg+I9+62ZqBrT1tQST3in/+L/tAxUcoRpSLNT0nGaPOoUaNy3X3zGRbKF+TGaduonW6PuEgrRpLiEy9bz9YQp/6jXvTBBx9MG220UR4pjK8YOWxe099cnCGI2uAoI4gZJOJ2fMVFpc1LY8rbESUWMeIYI6LT2/64eK1cDz4zHelzjGLFxVxx4WRcyFet7Wj9fLZnP5dHqmJ0P87E9OvXb5r7oxwrRpXbOyNPbHP0MUbsYp0xSj+rr7WOPm+z0ofWYp/FiGc8Z23VDZdr2kP5E17jdyse23rUO0oaYl0xorn88stP8zy0p614fcXvVvkx8XzFett6DbUlXm+xH6Ov06tVnlE/u3Jbm4t2oxyurddde7apvdsZr5PYx63PukV/Y7/H8rZmseno9kyvnRhxj9drW8vjNdvW9kf/4+L+eP6jLn56v9+z+9xPz/S2Pa41iX0VZ4Zai9dNvI80n5CgvB2xX+J3snxRbqwnzgrM6BOUY/ac2Edxpqt8MXVZnEmI9S699NIt+je994Dy8mi/rffAGbUFlSTsM8dqHZznBHNinwGAOZeafQAAKChhHwAACkoZD3Os9tZKdydzYp8BgDmXsA8AAAWljAcAAAqqKp+gG9Nilae9a88UbwAA0N2VSqU8vW9MI9yeKWcLG/Yj6Mfc4QAAUDSjRo1KQ4cOTXNt2I8R/fKOaM9HtAMAQHfX2NiYB7TLWXeuDfvl0p0I+sI+AABF0qMblal3j2IiAACg4oR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAADmEI133pkmPvd8ux/fK1XRK2uvk/rPM081uwAAQEGs9PJLM33MZ089lSb/+9+pdrPNUlf55OabU98VV8xfs+vDK65IA75en+ZbbdV2Pd7IPgAAc40JDQ3p42uu6dI2P/yfS9JnTzyZqqGqI/sAANBVJr7wQpr4/PNp8vv/Sh9dcUVe1n+TTdKXY8akqRMmpPnWXDN9+re/pakTJ6UFdtk53z9lwqd52ZRx49K8w4elfuuu22KdjX/+c5r8/vsp9eiRvujXLy3Wq2W8brz77jSlsTF99thjKU2ZnJctsOeeqUePHjNdd/ji7bfTp488mnottGDqt846Hd5mYR8AgLnC1PET/vP1+efpi/fe+8+yiRPT+HvuSRMa7suBfb4110i9F1883zfx2WfTqIMOTn1XWCH1HjIkfXTZZan30KFpiV9dnHr07p0fM+XDD/+zrlJKE99+O906bFiaeO+9qXb77fP9k8d+kEqTJ6cpn3zS1GYqldLE556b6brjQGL00cekmg03TD1r+qUxP/lpKn35ZYe2WdgHAGCuULP+V1LNBuvn0f1FTzihxX1fjh6dht10U+q7wvL5+wjV7x5xRBr8vUPTAv/933nZ1C++SG9uv0P6+Jpr06A9v52XLbDrrk3raGxsTEcssUQ66+xz0iL/G/bjcVE2NGCzzdKgPXZv97rjgGTMGWemhQ45OC104IH/Wf9dd6X3jjyqQ9ss7AMAMNfru/LKTUE/fPb002ny6PfT1E8/TR9d9fs8Gh9f88w/f/rs6aeawn6Y9NJLadIrr6TPxo5NS/buk6Z++GH6cuzY1Hvhhdvcr+1Z96QoNxo7tulgIAzYYos0z4/OEPYBAKAjeg0a1OL7yWPGpNSzZ67nb67vKiuneYcNa/p+9A+OTxPuuy/VbLhBmlLTPy30vzX7U8eNS2k6Yb89647H9Jh33nwAUBZ1/r0WWaRj29WhRwMAwFxgngjZU6emBffZJ/VedNE2HzPp1VfTuJtvTsvc85fUZ+jQXMZzzc/PSjvPP38qxWj9bKx7ngUXSqXPP8+j/z1rapqWxzUCHWHqTQAA5ho9a/qn0sRJM31cvxEjUs+BA9OHl13WYnm+uPedd/5ze8KEPELffPT9m7UDp22zf/9UmjSxQ+uOUf6etbVp3O13NN0fs/JEaU9HGNkHAGCu0W/tEemDiy5KY889L/VacFCeerMtMZq++M9+mi+I/eLNt1K/EWulyR98kD79+8Np4e8fl/osuWTqu+qqqc/SS6d39t03DajbNI1/9h9piwEDpm1zxIj08fV/yKP9Pfv0yVNvzmzd8/TvnwYfflgac8YZ6fNXX009+82Xxt12e4sDi/boUZrROYZOEqc4Bg4cmB5bdjmfoAsAQJd9gm749JFH0mePPZ6mTBif5t9hh/TFqFFpyscfpwV2/s/c+s19OWZsGv+nP6XJY8fkKTL7b/r11HuR/6vFnzJ+fBr3xz/+Z67+wYPTGvvtn5446cS0+CGHpF6DBzfNtNN4+x3pizffyKP3i/zgB6lHrtmf8bpzXx99LM/FH/Ps96+ryx8KNu/yy6eaDTZI7SHsAwAwV4X9zh7QHjduXKqtrU3dgZp9AAAoqKrW7K/w5BPd5qgHAACKxsg+AAAUlLAPAAAFJewDAEBBVbVmv6GhIdU0+0QwgLlNfX19tbsAQIEZ2QcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAAqqVzUbr6urS7W1tdXsAgAAFJaRfQAAKChhHwAACkrYBwCAgqpqzX5DQ0OqqampZhcAKqK+vt6eBKDbMbIPAAAFJewDAEBBCfsAAFBQwj4AABSUsA8AAAUl7AMAQEEJ+wAAUFDCPgAAFJSwDwAABSXsAwBAQQn7AABQUMI+AAAUVK9qNl5XV5dqa2ur2QUAACgsI/sAAFBQwj4AABRUVct4GhoaUk1NTTW7AMxl6uvrq90FAOgyRvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCgelWz8bq6ulRbW1vNLgAAQGEZ2QcAgIIS9gEAoKCqWsbT0NCQampqqtkFmEZ9fb29AgAUgpF9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoqF7VbLyuri7V1tZWswsAAFBYRvYBAKCghH0AACioqpbxNDQ0pJqammp2AbL6+np7AgAoHCP7AABQUMI+AAAUlLAPAAAFJewDAEBBCfsAAFBQwj4AABSUsA8AAAUl7AMAQEEJ+wAAUFDCPgAAFJSwDwAABSXsAwBAQQn7AABQUL2q2XhdXV2qra2tZhcAAKCwjOwDAEBBCfsAAFBQVS3jaWhoSDU1NdXsAnOJ+vr6ancBAKDLGdkHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAAAKqlc1G6+rq0u1tbXV7AIAABSWkX0AACgoYR8AAApK2AcAgIKqas1+Q0NDqqmpqWYXqIL6+nr7HQCgCxjZBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIKa5U/Qfffdd/NXWGKJJdKQIUMq2S8AAKArw36E+1/+8pfpmmuuSW+99VaL+4YPH5523XXXdOCBB6ahQ4fObr8AAICuKuP54Q9/mFZcccX0zDPPpGOPPTY99thj6e23385fcfuoo45KTz75ZH7MySefPLv9AgAAumpkf/To0enFF19MSy655DT3xbJ11103HXLIIemdd95Jp512WrvWWVdXl2prazvWYwAAoF16lEqlUupijY2NaeDAgWncuHHCPgAAhdDYDTOu2XgAAGBuL+PZb7/92r3SSy+9dFb7AwAAdHXYnzRpUtPtCRMmpFtuuSVPt7nWWmvlZU8//XR677330nbbbdfuxhsaGlJNTU1H+8wcor6+vtpdAACYq7U77F911VVNt/fff/90+OGHp7PPPjv16vWfVUyePDkdc8wx6bPPPuucngIAAJ3/oVp33HFHev7555uCfl5Rr155es7VV199VlYJAAB0hwt0x48fn+fXby2m3Yz7AACAOTTs77zzzmmnnXZK119/fXrzzTfTG2+8kW/Hsl122aXyvQQAALqmjOfCCy9Mxx9/fNpzzz3T559/npf17ds3HXjggeknP/nJrKwSAADoDmF/vvnmS7/4xS9ysI9R/TB8+PC8HAAAmIPDflmE+1VWWaVyvQEAAKr/CbrXXnttGjlyZFpmmWWalp1xxhlp7NixleobAADQ1WH/17/+dTrssMPSxhtv3FTGEwYNGpR+/OMfz05/AACAaob9c889N91www3p5JNPbrF8q622Stddd12l+gYAAHR12I/pNtddd918u0ePHk3LF1hggfTRRx/NTn8AAIBqhv0hQ4akF154YZqwH5+su+yyy1aqbwAAQFfPxnPQQQelvffeO5133nk57Efwv/vuu9Ppp5/eoXn26+rqUm1t7ax0AQAA6Iywf/TRR6fx48en7bbbLk2ZMiWtuuqqeRrO4447Lh188MGzskoAAKDCepRKpVJHf+jll19OK664Ypo0aVJ66aWX0tSpU9NKK62U+vXr13TfjDQ2NqaBAwemcePGGdkHAKAQGrthxp2lsB+lO9P7sRnd13pH3HzzzammpqajzTMHqK+vr3YXAADS3B72Z/lDtdoyYcIE4R0AAObEmv0jjjiizdshSnmeeeaZNGLEiMr1DgAA6Jqw/9prr7V5O/Tu3Tutvfba6fDDD5/13gAAANUJ+7fffnv+f6+99kqXX3555XoBAAB0j5r9t99+u/I9AQAAqh/2n3zyyXwxLgAAULCwv+WWW6Zrrrmm8r0BAACq+wm6AwYMSAcccEC68cYb08orr5z69OnT4v6f/vSnleofAADQlWH/rbfeSptuummaPHlyevbZZ2e1bQAAoLuF/XvuuafyPQEAALrvJ+gCAABz+Mh+ePPNN9P555+fXnrppVQqlXLtfnyg1rBhwyrbQwAAoOtG9v/617+mlVZaKd1///053C+zzDL5diyL+wAAgOrrUYph+Q5ad91109Zbb51OPfXUFsvj+zvuuCM9/vjjM/z5xsbGNHDgwDRu3LhUW1vb8V4DAEA309gNM+4shf155503jRkzJs0///wtln/88cdp0UUXTZ9//vkctyMAAGB2dMeMO0tlPAsssEB69dVXp1n+yiuvpEGDBlWiXwAAQDUu0N1jjz3SzjvvnM4444y03nrr5WWPPvpoOvHEE/N97dXQ0JBqampmpQt0I/X19dXuAgAAlQr7P/7xj1PPnj3Tfvvt11SyE6U9hx12WD4AAAAA5tCw36dPn3TWWWel008/Pb3++uupR48eafjw4alv376V7yEAANB1NftTpkxJzzzzTA73q6yySl520kknpYsvvjjPuQ8AAMyhI/vnnHNO+uSTT9Kaa66ZJk6cmEaOHJmGDBmSrrrqqvTBBx+kk08+ufI9BQAAOn9k/5JLLkkHHHBAvn3vvfemhRZaKD322GPpzjvvTJdffvmsrBIAAOgOYX/06NFp8ODB+XZ8Yu62226bb8cn6P7rX/+qbA8BAICuC/srrLBCuuyyy9I777yTrrvuurTZZpvl5TH3ftwHAADMoWH/zDPPTMcdd1xaaqml0ogRI9LGG2+cl1900UXp4IMPrnQfAQCArrpAd6uttkpjx47NX8OGDctTb4Z99tknrbvuurOySgAAoFoj+++++26L7/v375/n1i8H/bD++uuneeaZZ5rHAgAA3Tjsr7322unII49ML7/88nQf8/zzz6fDDz88l/YAAABzSBlPfIhWfHDWGmuskWv1I/wvssgi+UO0Ygaexx9/PL333ntpjz32SM8++2zn9hoAAJipHqUOfuRtBPubbropPfTQQ2nUqFG5jGfo0KFpo402SjvssEM+AJiZxsbGNHDgwHTzzTenmpqajjRPN1RfX1/tLgAAVF3j/2bccePGpdra2jRHhv2i7ggAAChaxp2lqTcBAIDuT9gHAICCmqV59iuloaFBzX43pQ4fAGDOZ2QfAAAKapbC/pQpU/JUnGUvvvhiOuaYY9LFF1+cp+IEAADm0DKec845J33yySdpzTXXTBMnTkwjR45MQ4YMSVdddVX64IMP0sknn1z5ngIAAJ0/sn/JJZekAw44IN++995700ILLZQee+yxdOedd6bLL798VlYJAAB0h7A/evToNHjw4Hz7r3/9a9p2223z7ZVWWil/6BYAADCHhv0VVlghXXbZZemdd95J1113Xdpss83y8ldffTXfBwAAzKFh/8wzz0zHHXdcWmqppdKIESPSxhtvnJdfdNFF6eCDD650HwEAgK66QHf48OFp7Nix+WvYsGGpR48eefk+++yTBgwYMCurBAAAukPYj9r8mGKzf//+LZavv/76OfibfhMAAAr2oVoTJkzwibgAADAnjuwfccQRbd4OU6dOzR+0FTX8AADAHBb2X3vttTZvh969e6e11147HX744ZXrHQAA0DVh//bbb8//77XXXj48CwAAurkepSpcTdvY2JgGDhyYxo0bl2pra7u6eQAAmCsybrtH9g899ND8/4UXXth0e3riMQAAwBwS9t999902bwMAAN1TVct4br75ZlN1diP19fXV7gIAwByrsRuW8VR0nn0AAGAO/wTdyZMnpyuuuCI99NBD6aOPPprm/hixBwAA5sCwH3Pp//73v09bb711Gjp0aOV7BQAAVCfsX3/99emee+5J66yzzuz3AAAA6D41+z179kwrrrhi5XsDAABUN+yPHDky3XTTTZXrBQAA0D3KePr27Zv22WefdMstt6Rll1029ejRo8X9P/3pTyvVPwAAoCvD/ltvvZU22WSTPIfok08+OattAwAA3S3sx8W5AABAgcL+pEmT2lzep0+ffNEuAADQfXQooc8333xtfvXr1y+tttpq+YO2AACAOXBk/8orr2xz+fjx49Ozzz6bDjrooNSrV6+0++67V6p/AADALOpRKpVKqUKuvvrqdN5556XHH398ho9rbGxMAwcOzBf41tbWVqp5AAComsZumHErWmi/9dZbp1dffbWSqwQAALpD2P/ss89yDT8AAFCwsH/BBRekNdZYo5KrBAAAuuIC3f3222+6F+g+99xz6Y033kgPPPBAu9fX0NCQampqOtIFOlF9fb39CwAwt4b9559/vs3lAwYMSJtuumm6+eab0/LLL1+pvgEAAF0V9h955JHZaQsAAOhCPvYWAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAgupVzcbr6upSbW1tNbsAAACFZWQfAAAKStgHAICCEvYBAKCgqlqz39DQkGpqaqrZBf5XfX29fQEAUDBG9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgILqVc3G6+rqUm1tbTW7AAAAhWVkHwAACkrYBwCAgqpqGU9DQ0OqqampZhfmGvX19dXuAgAAXczIPgAAFJSwDwAABSXsAwBAQQn7AABQUMI+AAAUlLAPAAAFJewDAEBBCfsAAFBQwj4AABSUsA8AAAUl7AMAQEEJ+wAAUFDCPgAAFFSvajZeV1eXamtrq9kFAAAoLCP7AABQUMI+AAAUVFXLeBoaGlJNTU01u9Bt1NfXV7sLAAAUjJF9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoqF7VbLyuri7V1tZWswsAAFBYRvYBAKCghH0AACioqpbxNDQ0pJqammp2oduor6+vdhcAACgYI/sAAFBQwj4AABSUsA8AAAUl7AMAQEEJ+wAAUFDCPgAAFJSwDwAABSXsAwBAQQn7AABQUMI+AAAUlLAPAAAFJewDAEBBCfsAAFBQvarZeF1dXaqtra1mFwAAoLCM7AMAQEEJ+wAAUFBVLeNpaGhINTU1aW5QX19f7S4AADCXMbIPAAAFJewDAEBBCfsAAFBQwj4AABSUsA8AAAUl7AMAQEEJ+wAAUFDCPgAAFJSwDwAABSXsAwBAQQn7AABQUMI+AAAUVK9qNl5XV5dqa2ur2QUAACgsI/sAAFBQwj4AABSUsA8AAAVV1Zr9hoaGVFNTk7qz+vr6ancBAABmiZF9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoHpVs/G6urpUW1tbzS4AAEBhGdkHAICCEvYBAKCghH0AACioqtbsNzQ0pJqamqq1X19fX7W2AQCgsxnZBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACqpXNRuvq6tLtbW11ewCAAAUlpF9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICC6lWNRkulUv6/sbGxGs0DAEDFlbNtOevOtWH/ww8/zP8vscQS1WgeAAA6zfjx49PAgQPTXBv2Bw0alP9/5513us2OKPIRZhxUjRo1KtXW1la7O4VlP9vXReR1bT8Xjde0fd3ZYkQ/gv7iiy+euouqhP2ePf9zqUAEfQG0a8R+tq/t5yLxmravi8Zr2r4uornxdT2wmw1ku0AXAAAKStgHAICCqkrYn3feedMpp5yS/8e+LgKvafu6iLyu7eei8Zq2r+dGPUrdaW4gAACgYpTxAABAQQn7AABQUMI+AAAUVJfPs//555+nF154IdXU1KQVVlihq5ufq0yYMCH94x//SEsttVQaOnRotbtTWJMnT06vvPJK6tu3b1p66aXTPPPMU+0uFdYXX3yRXn755fz+YV93jSeeeCK/xtdff/0uanHuER8sGV/N9e7dO33lK1+pWp+KbuLEifk9JD7waJFFFql2dwrn/fffT6+//nqb962zzjr57yQFD/t33XVX+va3v50/bODjjz9Ow4cPT7fddltabLHFurIbhffuu++mn/zkJ+mmm25KH374YTr99NPTD37wg2p3q3C+/PLLvG9/9atf5T8a48aNy3+oL7300rTppptWu3uF29cnn3xy3rdx4DpmzJg8q8ZvfvMb+7oTXX/99WnXXXdNAwYMSJ988klnNjVXuuyyy9J5552XVltttaZl888/f7r99tur2q+i+vnPf57fs2Og4LPPPkt1dXX5/btXr6p8vmghPfzww+ncc89tseyNN95IH3zwQX7fFvYLXsbz73//O+2yyy7p8MMPz0d9o0ePzsFo33337aouzDVi/6688sp5tHnhhReudncKPUIUgTP29/PPP5/efvvt9M1vfjPtsMMO+SPZqey+jkGBUaNGpaeffjof0G6xxRb5PcWEYp3jzTffTEcddVQ66KCDOqkFQgT9v/3tb01fgn7nuOiii9Kpp56aBx2fe+65/L698cYb52oDKif+/jV/PcfX4MGD07bbbpsGDRpkVxc97P/hD39IU6ZMSUcffXT+Po7ujjnmmHT33Xfn0z5Uzte+9rV0yCGHzHUfT93VYv+edNJJTfu5Z8+e6YADDsgj/BH+qey+Puyww5pGhWJff/WrX00fffRRmjRpkl1dYVG2EyP6p512Wlp22WXt304+a/XMM8+kV199Ne93Ki/2a4zoH3rooWmjjTZqWv6d73wnlwTSuWWAzz77bNp///3t5rkh7Mdo3EorrZT69evXtGy99dbLo3LxRgdF8Pjjj6cePXrkEjUq75///Gd68MEH05VXXpnLek488cQ033zz2dUVFvs1apqdee2aMLT77rvnkpI4e3X11Vd3QatzlxjJHzt2bB5djv+feuqpXEpM54tSyyWWWCJtvvnmdncVdVmhWozALbjggi2Wlb+P+2BOF6UlcbYqRjAWXXTRanenkOIMYVznEzWgceH5jjvuWO0uFc6f//zn9Pvf/z5f3E/nigtxo1xqySWXzANf55xzTtpzzz3Tcsstl9Zdd127v0KibDjccMMN6dprr80HVXGR7h577JFr9k2q0Hnll9dcc0068sgj89lYqqfL9n7U57c+3R4vhNCnT5+u6gZ0irj4KEYuVl111XT++efby53khBNOyBeAxR/vqLfdZJNNjNBVuKQkwuZ+++2XXnrppVxvG2E0SjDjdlxgR+VsueWWOeiHOCMYgwXDhg3LoZTK5o8Q5ZXxeo5Kgxjdj+B/4YUX2tWdJF7H48ePT/vss499PLeE/RiFe++991osK39ffrODOVFcfB6z78SMPLfeeqvZBrpAjMTFxaNxKv7RRx/tiibnmtrmqNG/55578gxe8RVnUmJgJm5HmRqdKyZVaP23ktkTs++Ua/TLZX9RVhzX/URZIJ1XwhMTKUQZD3NJ2B85cmS++v3FF19sWnbLLbfkq7NHjBjRVd2ATgn6MdtAzKLR/JoUKufTTz+dZtlrr72W/29dHsisiyDUeiaNuDC6f//++fY222xj93bi6zrOnMTFjHGGkMpZfvnl8xmTtgYc472byov35wceeMCFuXNbzf7Xv/71tNlmm6Wddtopz/AQp+HPPPPMPMdw+RQblRGjcE8++WTThxDFlJDxh3qBBRZIq6yyit1cITFPcxzExvzjZ599dj4tXBajRkJo5cSBVFyUG+8fQ4YMySUmP/vZz3L4VNvMnDxz2re+9a205ppr5lLAs846K18YfeCBB1a7a4WcYz8uOI8D17gm4sYbb8wX/LsguvNG9eNstwGC7qFHqQsnqY5wFB+2EEd7MQIaMxDEH28qKz6RcbfddptmeXwCZoRSKiNGhWKe97acccYZuZ6cyrn//vtz4I+D1whEW221VX7/cOFX518UHR/+FPOTU1kxOUXUjEcpWkwBGRfsHnzwwWaY6iRRnhYfzBf7PUb7jzjiCFPLdpKdd945bbjhhnkfM5eFfQAAoOuYCwkAAApK2AcAgIIS9gEAoKCEfQAAKChhHwAACkrYBwCAghL2AQCgoIR9AAopPiU1PvwOYG7mQ7WAOd4XX3yRbrrppqbv559//rTSSiulpZZaqiLrf/jhh1OvXr3SuuuuO819r7zyShozZkz66le/mrrarbfemj+ZPMSnkg8fPjytuuqq031cfX19WmihhVrc99BDD6VRo0altdZaK62wwgpNyz/++OP0j3/8I//caqutlpZYYonpth2f/rrMMsuklVdeuc37m4s2oq3O+KTf//qv/8qfrlzWv3//dNVVV6VvfvObFW8PYE4h7ANzvH//+99p8ODBaeONN85h78MPP0wPPPBA2n///dOFF1442+v/7//+7xwcL7300mnu23333XPI/tGPfpS62tChQ9OgQYNyyP7000/T3/72t7TmmmumO+64I4f/5o+LEe7TTjstnXzyyU3Lv/zyyzRkyJD0wQcfpJ///OfpmGOOyct/+ctfpu9///tplVVWyfv1xRdfzG38+te/Tosuuug0bU+YMCHv7/XXXz+H/L59+7a4v7mtttoq7bnnnhXfF9HmDTfckLbZZpumZXvttVc69NBD0zrrrFPx9gDmFL2q3QGASjnuuOOawl4E3ri97bbbps033zxNnjw5j9DHgUGM+q+44orT/Pzrr7+eg20E2hh9jtH8p556Kr3zzjs5TF577bX5cbG+BRZYIE2ZMiXdfffd6c4778zLI2xHyI5R7qeffjpNmjQpfe1rX0vzzTdfUxvlx8QBwiOPPJLmmWeeNHLkyHxfPP7vf/97mjhxYh5NX3LJJWe6zTvvvHM66aST8u3oZ2zb+eefn44//vgWj4szD7/97W/TD3/4w9SjR4+87JZbbsmBvGfP/6vofOONN9L3vve99Lvf/S4fyJTddddd+YBiem2/9tprafXVV89tx4FC6/vbI/ZvXV1dbifOKsRZgDhYiOdy/PjxuZ9xcBLPTfODmTjAmDp1anrwwQfzgUfc941vfCM/94sttliLNuKMRbwO4vEbbLBBWnDBBdvdP4A5kbAPFFKMIPfp0yc988wzOdhHQI9yn+WXXz4H6p122in95je/aXr8sccem0eu4+xAnBmIUe+bb745h84YFY/gH9+H9dZbL4f9WE/z8p6zzz47jR49Opf1RJtx8BAHBA0NDWnppZduesz777+fDzrKJS0R9uMgIPoUAT9G02PdBx54YPrxj3/c7m2On43R+OhzW/sjgvi9996by3lCnKnYd9990znnnNP0uDjYiSD89a9/vcXPb7nlljNse9lll81tx/6eVbvuumtu5+WXX877ZY899shh/y9/+Uv617/+lfsVZVMR2CPgx1mM8Kc//Snv5wjxb7/9dg7wEfa/853v5DKeOEAIUeoVy+JAKg4coq+xD+LMDUBRCftAIUXoi3AfI7tHHHFEHq2PUDjvvPPmQDtixIh8ABCjz+PGjcuB99FHH20K7s8991weYd97773zz7VVxnP77bfnEN18ZDzOBMR61l577XzAEG0cffTR+WLRslh3BPLlllsufx+j1ttvv30677zzcsAt93+NNdZIm266aVM4n5nY3qi/j3Ka1uIMQgTdyy67LK8vzgLcd9996YorrmgR9qPNOItxyCGHpB/84AdNZzhm5vPPP8/rjIOl5ttZPhtStuGGG87wjEVjY2N+fqIPZb/4xS9aPOaoo45KRx55ZD6IChdddFE+cGt+Zqe1OECIsq4401A+8xDbfcABB+QDmzjAAigiYR8ojHIZx0cffZQDYIw2RxiP2u0oWYmgH2K0OMJ1BNEI+xGEe/funZ599tlc3x1lLjH6OzMR9k8//fQWy6IMJYJ+iHVGKI0LRCOIx5mGsMUWWzQF/XDbbbfl0pXoX1xoGkqlUj4bEIF2RmG/HKjjYtjrr78+/9zhhx/e5mP32WefXGoTwTdCf4yiL7LIIi0eExfiRn+iDOgrX/lK7lNc+BpBeZdddmmz7ej7Nddck/tw0EEHNd3/0ksvNZUMlcVF0zMK+9/97ndbBP3m5UUxqh8HA3Hg9dhjj6WO+POf/5wP3uLAr+ywww7L1zFEiVJnXEcA0B0I+0BhlMs4Bg4cmEdsY1Q+SmYiAEeNfHNRVx8hL0R4jHr2GMmOkd9NNtkk16tPb5Q4vPXWW7lOfbPNNmuxvFyuUzZs2LBcfvLuu+829aF1HXmsK0bPm4/+hygFmtmMQuVA/cknn+QDg7hQOLatLXGAESVIUdoS2xsHRG2Jg4v4igt34yzFddddl0td4vu44LV121EjHwc5UefffDacjtbst7VvYt/FGYk4WIu+xzUGcbASBxZxkBEzAbVHvC6inKd8wFc+GIsDj7gPoKiEfaAw2irjiAtzQ4z2NxffN5+GcrfddstfEWBjxD5GsS+44II8Gt6WGP2Oi14HDBjQYnkE0ba+b95W69Hu2traHGqvvvrqFiVB7dE8UMeMOFH2EwcJUbPelqjRjxHtCOgzq8OP0pbYn/EV1xhE/5qH/VkJ8zPTet/cc889udY+rn8ozwQUz08sj4O49or93/o10NbrAKBofKgWUGhxIW3MUNN8Hv6oL4+wvtFGGzXVzMdXiMfGxboxsh2z5ZRH/mOmnOYicLY18h8XwJbXFaLdmPs+Av30xNmBKDGJEffmovQnQnZ7xcFHlNtEqUr8bFt23HHHXNoUZwCifKm1aC9KoVqLfRafX9DV4sLcaLd5uVFMsdlaW89R62sF4uzH/fff37QsLoKO9cd9AEVlZB8ovLjAM4J5XDAbtfhXXnllLueIevpywI0ZcXbYYYdczx+jyDEDTPkAIer4TznllHTJJZfk0B4HCREaYz761iJAx+h6jKDHWYJ4TJSgzEjMEBThO4L6E088kS+SjdKeCLVxUXBHRp6jn7F9F198cZu1+zGiHyP00xO18TEr0NZbb533VWxPlDvFh2/Fhcod0dYFujH/fvkgqz1iX8bBU9TUR6nQX//61zwVZ2vxHEVZUgT+eI5an9mIsx1x0XEc7MQZoDiDctZZZ+V9HvsboKiEfWCOF8E9ym7KUyy2NXIetecRgiO0xvzrcSFojAaX6+pjFD/q2KMUJspX4mLf8oW2++23X/4/LgqNUe8ozYmfaas2Pmrb4wLcOFiI0fAIp81nqInbUXfe2gknnJDDbNTtR9txABBz+M+oZn+77bbL0102F6UuMatPXGzc/HFtfa5AWVxAXL4/6uLjZyOkx4xBMaVlXKgb03Y2v7C2rbZb9y2mMC1PV1oWsx1NL+zHc1gu1Wl+cBDPXUyLGgdYEczjzEVMYRo192WXX355PrCKA5Jy2I9gHz9fFtsQbcdzEyVAcRDY+qJjgKLxCboAHRSj9hHY41NnW4fmCJeV+NReAKgENfsAHRQX5cYHQAFAd6eMB6CDWn/I08xKdACgWpTxAABAQSnjAQCAghL2AQCgoIR9AAAoKGEfAAAKStgHAICCEvYBAKCghH0AACgoYR8AAApK2AcAgFRM/x/+KILl2bjbKAAAAABJRU5ErkJggg==", + "image/png": 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", "text/plain": [ "
" ] @@ -629,10 +629,10 @@ "id": "94bda60c", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:09.078137Z", - "iopub.status.busy": "2026-10-06T08:04:09.077749Z", - "iopub.status.idle": "2026-10-06T08:04:09.088271Z", - "shell.execute_reply": "2026-10-06T08:04:09.087192Z" + "iopub.execute_input": "2026-11-01T23:35:46.930218Z", + "iopub.status.busy": "2026-11-01T23:35:46.930155Z", + "iopub.status.idle": "2026-11-01T23:35:46.932858Z", + "shell.execute_reply": "2026-11-01T23:35:46.932557Z" } }, "outputs": [ @@ -640,7 +640,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "90% confidence set for a constant effect: [3.47, 20.45]\n" + "90% confidence set for a constant effect: [3.39, 31.50]\n" ] } ], @@ -670,10 +670,10 @@ "id": "723eadfa", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:09.090647Z", - "iopub.status.busy": "2026-10-06T08:04:09.090346Z", - "iopub.status.idle": "2026-10-06T08:04:10.139373Z", - "shell.execute_reply": "2026-10-06T08:04:10.138343Z" + "iopub.execute_input": "2026-11-01T23:35:46.933966Z", + "iopub.status.busy": "2026-11-01T23:35:46.933910Z", + "iopub.status.idle": "2026-11-01T23:35:47.177915Z", + "shell.execute_reply": "2026-11-01T23:35:47.177528Z" } }, "outputs": [ @@ -686,7 +686,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -723,10 +723,10 @@ "id": "49f1875e", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:10.141466Z", - "iopub.status.busy": "2026-10-06T08:04:10.141251Z", - "iopub.status.idle": "2026-10-06T08:04:10.730444Z", - "shell.execute_reply": "2026-10-06T08:04:10.729452Z" + "iopub.execute_input": "2026-11-01T23:35:47.179117Z", + "iopub.status.busy": "2026-11-01T23:35:47.179057Z", + "iopub.status.idle": "2026-11-01T23:35:47.348503Z", + "shell.execute_reply": "2026-11-01T23:35:47.348143Z" } }, "outputs": [ @@ -736,13 +736,13 @@ "text": [ " placebo_period placebo_att status\n", " 20 0.150 ran\n", - " 35 0.547 ran\n", + " 35 0.777 ran\n", " 50 0.916 ran\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -808,10 +808,10 @@ "id": "2a9ee973", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:10.733398Z", - "iopub.status.busy": "2026-10-06T08:04:10.733158Z", - "iopub.status.idle": "2026-10-06T08:04:10.745197Z", - "shell.execute_reply": "2026-10-06T08:04:10.744351Z" + "iopub.execute_input": "2026-11-01T23:35:47.349778Z", + "iopub.status.busy": "2026-11-01T23:35:47.349700Z", + "iopub.status.idle": "2026-11-01T23:35:47.355558Z", + "shell.execute_reply": "2026-11-01T23:35:47.355088Z" } }, "outputs": [ @@ -854,10 +854,10 @@ "id": "3625338c", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:10.748515Z", - "iopub.status.busy": "2026-10-06T08:04:10.748049Z", - "iopub.status.idle": "2026-10-06T08:04:11.458833Z", - "shell.execute_reply": "2026-10-06T08:04:11.457749Z" + "iopub.execute_input": "2026-11-01T23:35:47.356457Z", + "iopub.status.busy": "2026-11-01T23:35:47.356396Z", + "iopub.status.idle": "2026-11-01T23:35:47.583945Z", + "shell.execute_reply": "2026-11-01T23:35:47.583507Z" } }, "outputs": [ @@ -887,16 +887,16 @@ "id": "432d5ad4", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:11.461220Z", - "iopub.status.busy": "2026-10-06T08:04:11.460873Z", - "iopub.status.idle": "2026-10-06T08:04:11.651502Z", - "shell.execute_reply": "2026-10-06T08:04:11.650342Z" + "iopub.execute_input": "2026-11-01T23:35:47.584990Z", + "iopub.status.busy": "2026-11-01T23:35:47.584927Z", + "iopub.status.idle": "2026-11-01T23:35:47.645643Z", + "shell.execute_reply": "2026-11-01T23:35:47.645249Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -943,10 +943,10 @@ "id": "4847e971", "metadata": { "execution": { - "iopub.execute_input": "2026-10-06T08:04:11.654621Z", - "iopub.status.busy": "2026-10-06T08:04:11.654335Z", - "iopub.status.idle": "2026-10-06T08:04:11.838251Z", - "shell.execute_reply": "2026-10-06T08:04:11.837266Z" + "iopub.execute_input": "2026-11-01T23:35:47.646813Z", + "iopub.status.busy": "2026-11-01T23:35:47.646749Z", + "iopub.status.idle": "2026-11-01T23:35:47.716505Z", + "shell.execute_reply": "2026-11-01T23:35:47.716197Z" } }, "outputs": [ @@ -1051,7 +1051,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.7" + "version": "3.14.4" } }, "nbformat": 4, diff --git a/docs/tutorials/27_cic_distributional_effects.ipynb b/docs/tutorials/27_cic_distributional_effects.ipynb index a12de0295..847197e77 100644 --- a/docs/tutorials/27_cic_distributional_effects.ipynb +++ b/docs/tutorials/27_cic_distributional_effects.ipynb @@ -61,10 +61,10 @@ "id": "8c425afc", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:39.810008Z", - "iopub.status.busy": "2026-07-18T18:33:39.809770Z", - "iopub.status.idle": "2026-07-18T18:33:40.741298Z", - "shell.execute_reply": "2026-07-18T18:33:40.740870Z" + "iopub.execute_input": "2026-11-01T23:35:48.712474Z", + "iopub.status.busy": "2026-11-01T23:35:48.712125Z", + "iopub.status.idle": "2026-11-01T23:35:49.406489Z", + "shell.execute_reply": "2026-11-01T23:35:49.406088Z" } }, "outputs": [], @@ -108,10 +108,10 @@ "id": "c813b304", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:40.742440Z", - "iopub.status.busy": "2026-07-18T18:33:40.742344Z", - "iopub.status.idle": "2026-07-18T18:33:40.751858Z", - "shell.execute_reply": "2026-07-18T18:33:40.751500Z" + "iopub.execute_input": "2026-11-01T23:35:49.407979Z", + "iopub.status.busy": "2026-11-01T23:35:49.407884Z", + "iopub.status.idle": "2026-11-01T23:35:49.413806Z", + "shell.execute_reply": "2026-11-01T23:35:49.413388Z" } }, "outputs": [ @@ -181,16 +181,16 @@ "id": "88f3a7f6", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:40.752863Z", - "iopub.status.busy": "2026-07-18T18:33:40.752782Z", - "iopub.status.idle": "2026-07-18T18:33:41.017084Z", - "shell.execute_reply": "2026-07-18T18:33:41.016669Z" + "iopub.execute_input": "2026-11-01T23:35:49.414783Z", + "iopub.status.busy": "2026-11-01T23:35:49.414729Z", + "iopub.status.idle": "2026-11-01T23:35:49.644903Z", + "shell.execute_reply": "2026-11-01T23:35:49.644560Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -233,10 +233,10 @@ "id": "2b28ac74", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.017955Z", - "iopub.status.busy": "2026-07-18T18:33:41.017890Z", - "iopub.status.idle": "2026-07-18T18:33:41.022664Z", - "shell.execute_reply": "2026-07-18T18:33:41.022284Z" + "iopub.execute_input": "2026-11-01T23:35:49.646087Z", + "iopub.status.busy": "2026-11-01T23:35:49.646023Z", + "iopub.status.idle": "2026-11-01T23:35:49.649569Z", + "shell.execute_reply": "2026-11-01T23:35:49.649077Z" } }, "outputs": [ @@ -319,10 +319,10 @@ "id": "b259b612", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.023531Z", - "iopub.status.busy": "2026-07-18T18:33:41.023475Z", - "iopub.status.idle": "2026-07-18T18:33:41.189465Z", - "shell.execute_reply": "2026-07-18T18:33:41.188983Z" + "iopub.execute_input": "2026-11-01T23:35:49.650694Z", + "iopub.status.busy": "2026-11-01T23:35:49.650621Z", + "iopub.status.idle": "2026-11-01T23:35:49.807161Z", + "shell.execute_reply": "2026-11-01T23:35:49.806841Z" } }, "outputs": [ @@ -381,16 +381,16 @@ "id": "0d52fd55", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.190332Z", - "iopub.status.busy": "2026-07-18T18:33:41.190272Z", - "iopub.status.idle": "2026-07-18T18:33:41.427982Z", - "shell.execute_reply": "2026-07-18T18:33:41.427570Z" + "iopub.execute_input": "2026-11-01T23:35:49.808355Z", + "iopub.status.busy": "2026-11-01T23:35:49.808296Z", + "iopub.status.idle": "2026-11-01T23:35:50.031696Z", + "shell.execute_reply": "2026-11-01T23:35:50.031332Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -461,10 +461,10 @@ "id": "87b44d77", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.429002Z", - "iopub.status.busy": "2026-07-18T18:33:41.428932Z", - "iopub.status.idle": "2026-07-18T18:33:41.477026Z", - "shell.execute_reply": "2026-07-18T18:33:41.476615Z" + "iopub.execute_input": "2026-11-01T23:35:50.032869Z", + "iopub.status.busy": "2026-11-01T23:35:50.032801Z", + "iopub.status.idle": "2026-11-01T23:35:50.077745Z", + "shell.execute_reply": "2026-11-01T23:35:50.077423Z" } }, "outputs": [ @@ -478,7 +478,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -543,10 +543,10 @@ "id": "3337fdbe", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.477961Z", - "iopub.status.busy": "2026-07-18T18:33:41.477898Z", - "iopub.status.idle": "2026-07-18T18:33:41.645718Z", - "shell.execute_reply": "2026-07-18T18:33:41.645289Z" + "iopub.execute_input": "2026-11-01T23:35:50.078869Z", + "iopub.status.busy": "2026-11-01T23:35:50.078812Z", + "iopub.status.idle": "2026-11-01T23:35:50.234893Z", + "shell.execute_reply": "2026-11-01T23:35:50.234529Z" } }, "outputs": [ @@ -567,9 +567,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "diff_diff/changes_in_changes.py:981: UserWarning: Treated pre-period outcomes fall outside the control pre-period support (Athey-Imbens Assumption 3.4 violated). The counterfactual distribution is only partially identified (Corollary 3.1): quantile effects are reliable only inside the reported (q_lower, q_upper) interior range, and the ATT involves extrapolation at the support edges.\n", + "diff_diff/changes_in_changes.py:995: UserWarning: Treated pre-period outcomes fall outside the control pre-period support (Athey-Imbens Assumption 3.4 violated). The counterfactual distribution is only partially identified (Corollary 3.1): quantile effects are reliable only inside the reported (q_lower, q_upper) interior range, and the ATT involves extrapolation at the support edges.\n", " _check_support(cells)\n", - "diff_diff/changes_in_changes.py:1324: UserWarning: Quantile effects at [0.05, 0.1, 0.15] lie outside the point-identified interior range (0.1698, 0.9956) (Athey-Imbens eq. 17 / Theorem 5.3). Point estimates are reported for qte parity, but their inference fields are set to NaN.\n", + "diff_diff/changes_in_changes.py:1349: UserWarning: Quantile effects at [0.05, 0.1, 0.15] lie outside the point-identified interior range (0.1698, 0.9956) (Athey-Imbens eq. 17 / Theorem 5.3). Point estimates are reported for qte parity, but their inference fields are set to NaN.\n", " return _fit_distributional(\n" ] } @@ -634,10 +634,10 @@ "id": "b2397a47", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.646801Z", - "iopub.status.busy": "2026-07-18T18:33:41.646733Z", - "iopub.status.idle": "2026-07-18T18:33:41.650456Z", - "shell.execute_reply": "2026-07-18T18:33:41.650137Z" + "iopub.execute_input": "2026-11-01T23:35:50.236029Z", + "iopub.status.busy": "2026-11-01T23:35:50.235958Z", + "iopub.status.idle": "2026-11-01T23:35:50.239488Z", + "shell.execute_reply": "2026-11-01T23:35:50.239044Z" } }, "outputs": [ @@ -688,10 +688,10 @@ "id": "ddc89e24", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.651301Z", - "iopub.status.busy": "2026-07-18T18:33:41.651240Z", - "iopub.status.idle": "2026-07-18T18:33:41.655230Z", - "shell.execute_reply": "2026-07-18T18:33:41.654902Z" + "iopub.execute_input": "2026-11-01T23:35:50.240491Z", + "iopub.status.busy": "2026-11-01T23:35:50.240422Z", + "iopub.status.idle": "2026-11-01T23:35:50.244226Z", + "shell.execute_reply": "2026-11-01T23:35:50.243908Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "9a6de829", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.656125Z", - "iopub.status.busy": "2026-07-18T18:33:41.656065Z", - "iopub.status.idle": "2026-07-18T18:33:41.661779Z", - "shell.execute_reply": "2026-07-18T18:33:41.661453Z" + "iopub.execute_input": "2026-11-01T23:35:50.245225Z", + "iopub.status.busy": "2026-11-01T23:35:50.245169Z", + "iopub.status.idle": "2026-11-01T23:35:50.250752Z", + "shell.execute_reply": "2026-11-01T23:35:50.250385Z" } }, "outputs": [ @@ -850,10 +850,10 @@ "id": "b4fce306", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:33:41.662858Z", - "iopub.status.busy": "2026-07-18T18:33:41.662802Z", - "iopub.status.idle": "2026-07-18T18:34:43.494364Z", - "shell.execute_reply": "2026-07-18T18:34:43.493886Z" + "iopub.execute_input": "2026-11-01T23:35:50.251913Z", + "iopub.status.busy": "2026-11-01T23:35:50.251840Z", + "iopub.status.idle": "2026-11-01T23:36:49.125242Z", + "shell.execute_reply": "2026-11-01T23:36:49.124681Z" } }, "outputs": [ @@ -943,10 +943,10 @@ "id": "ae0c2036", "metadata": { "execution": { - "iopub.execute_input": "2026-07-18T18:34:43.495544Z", - "iopub.status.busy": "2026-07-18T18:34:43.495470Z", - "iopub.status.idle": "2026-07-18T18:34:43.497732Z", - "shell.execute_reply": "2026-07-18T18:34:43.497314Z" + "iopub.execute_input": "2026-11-01T23:36:49.126609Z", + "iopub.status.busy": "2026-11-01T23:36:49.126530Z", + "iopub.status.idle": "2026-11-01T23:36:49.128656Z", + "shell.execute_reply": "2026-11-01T23:36:49.128330Z" } }, "outputs": [ @@ -1034,7 +1034,7 @@ " >>> # n_bootstrap=200 is the default; seed=42 is illustrative (default seed=None)\n", " >>> # carry over quantiles=/alpha= if you customized them\n", " >>> did_results = DifferenceInDifferences().fit(\n", - " >>> data, outcome='y', treatment='treated', time='post')\n", + " >>> data, outcome='y', treatment='treated', post='post')\n", " >>> print(results.att, qdid_results.att, did_results.att)\n", "\n", "============================================================\n", diff --git a/docs/user_guide.rst b/docs/user_guide.rst index ce48c3103..a3edb0dc4 100644 --- a/docs/user_guide.rst +++ b/docs/user_guide.rst @@ -7,7 +7,7 @@ User Guide The methods behind the library: scholarly references, validation against R and Python implementations, performance benchmarks, the migration guide for -the upcoming 4.0 release, and the methodology registry documenting every +the 4.0 release, and the methodology registry documenting every estimator's equations and edge cases. .. grid:: 1 2 2 3 @@ -37,7 +37,7 @@ estimator's equations and edge cases. :link: migration-4.0 :link-type: doc - Every breaking change in the upcoming 4.0 release, with the one-line + Every breaking change in the 4.0 release, with the one-line fix for each and a codemod table for the mechanical renames. .. grid-item-card:: Benchmarks diff --git a/docs/v4-deprecations.yaml b/docs/v4-deprecations.yaml index f972f16ec..051afc047 100644 --- a/docs/v4-deprecations.yaml +++ b/docs/v4-deprecations.yaml @@ -11,7 +11,8 @@ # # FORWARD-REFERENCE NOTE (for reviewers): `new` locators on `planned` rows name # 3.9/4.0 surface that intentionally does NOT exist at HEAD; the enforcement -# test asserts their ABSENCE until the owning phase ships. +# test asserts their ABSENCE until the owning phase ships. (Historical since 4.0.0: +# no `planned` rows remain.) # # FORMAT CONTRACT (parsed by a purpose-built scanner, no PyYAML): an optional # HEADER of single-line version scalars above `rows:` (today the cut-window @@ -30,6 +31,8 @@ # over many PRs; `__version__` must stay at `cut_base` until the release PR bumps # to `cut_window` (any other bump fails tests/test_v4_matrix.py's off-base # guard). The keys stay as a historical record once the window has closed. +# CLOSED 2026-11-01: the release PR (item 10) bumped __version__ to 4.0.0; keys +# kept as the historical record. cut_window: "4.0" cut_base: "3.13.0" diff --git a/docs/v4-design.md b/docs/v4-design.md index 862a6dfd8..206494d3a 100644 --- a/docs/v4-design.md +++ b/docs/v4-design.md @@ -1,6 +1,8 @@ # diff-diff 4.0 Design Specification -**Status: LIVING - normative until the 4.0 cut.** +**Status: CLOSED at 4.0.0 (2026-11-01) - the historical record of the 4.0 program. Normative +through the cut; no longer binding on post-4.0 work except where a section says so (the +section 8 naming rules remain the live vocabulary contract via `tests/test_naming_guard.py`).** **Review contract:** every Phase 2-5 PR of the 4.0 program is reviewed against this document. A PR that deviates from it must edit this document (and the @@ -11,13 +13,16 @@ a review reject. intentionally do NOT exist at HEAD. `tests/test_v4_matrix.py` enforces their absence until their phase ships, and flips to enforcing their presence when the matrix row flips. Nothing in this document is an undocumented deviation; it is -the documentation. +the documentation. (Amended 2026-11-01, item 10: phases 1-5 have shipped, so every +3.9/4.0 symbol described here exists at HEAD; the phase-6 4.1 `event_study()` front door +is the one surface still described but not built - sketch only, specified in its own plan.) Companion artifacts: `docs/v4-deprecations.yaml` (the lifecycle ledger - single source of truth for every old/new/version/status fact; this prose never restates per-row lifecycle data and instead cites rows as `[M-###]`), -`tests/test_v4_matrix.py` (enforcement), ROADMAP.md "4.0 API unification" -(program pointer). +`tests/test_v4_matrix.py` (enforcement), ROADMAP.md "4.1 `event_study()` comparison +front door" (the program's one remaining queued item; the "4.0 API unification" entry +was retired at the release, 2026-11-01). All decisions below were locked with the maintainer on 2026-07-18 (six program decisions + eight naming-checkpoint decisions), with dated addenda: 2026-07-19 @@ -25,7 +30,9 @@ decisions + eight naming-checkpoint decisions), with dated addenda: 2026-07-19 consolidation-scope decisions appended to the section 8 checkpoint list) and 2026-10-18 (the 4.0-cut framing at v3.13.0: the cut-window rule in sections 2 and 11, the roster refresh in sections 3.1/3.5, the M-080 re-scope and the -M-008 decision in section 7, and the 4.0 cut sequence in section 9). +M-008 decision in section 7, and the 4.0 cut sequence in section 9), and 2026-11-01 +(item 10, the release amendments: window closed, `maint/3.13` recorded as never cut, +tutorial facts corrected). Sections 3-8 are the target surface; section 9 maps it onto PRs. --- @@ -80,7 +87,10 @@ declares (`cut_window: "4.0"`, `cut_base: "3.13.0"`; mechanics in section 11). only by the release PR, which closes the window. Because main carries removals from the first cut PR onwards, 3.13.x patches come from a `maint/3.13` branch cut at tag `v3.13.0` BEFORE the first cut PR merges (fix lands on main, -cherry-pick back) - the maint/3.8 rule re-instantiated one major later. +cherry-pick back) - the maint/3.8 rule re-instantiated one major later. (Spent +2026-11-01: the release PR bumped `__version__` to 4.0.0 and closed the window. No +`maint/3.13` branch was cut - maintainer decision 2026-10-18, reaffirmed after PR 2; no +3.13.x patch was requested.) **Warning categories:** all NEW shims emit `FutureWarning` (visible to end users by default). Pre-existing `DeprecationWarning` sites keep their category @@ -1072,7 +1082,7 @@ above; anything only one PR cares about stays in that PR's plan.** | 2: contract foundations | 3.9 | (a) results base + unified event-study representation [M-092] + to_dict completion + the Diagnostic marker base on the diagnostic result roster [M-091] (section 3.5); (b) `aggregate()` + fit(aggregate=) shims [M-020..M-027] [M-139] (M-020's shim already shipped; M-139 is the HAD workflow twin, a pre-cut amendment); (c) param renames [M-030..M-047] [M-084] [M-086..M-089] + their results-field mirrors [M-094] [M-095] (section 8 rule 9) + the public-function completeness sweep [M-097..M-113] (section 8 rule 10) + the dCDH results mirror [M-114] + the fourth `robust` site [M-115] + the 2(c)-ii missed-rename amendments [M-136..M-138] (LPDiD `level` value; the two post-dummy diagnostics params) + BaseEstimator mixin + ContinuousDiD covariates move; (d) alias introduction [M-062] (the Spillover introduction is cancelled [M-063]) + the alias-diet `__getattr__` warning shim [M-135] + wrapper deprecations [M-070..M-077] + the two inference-surface policies: `n_bootstrap` semantic unification [M-081] and the wild-cluster-bootstrap roster guard [M-096]; shipped insertions (all done): the aggregate contract [M-122], the ETWFE reference-period family [M-123] [M-124] [M-125], and the variance-consolidation program [M-126] [M-127] | | 3: merges | 3.9 | (a) TWFE event-study mode [M-010] + EventStudy warn [M-060] + the fit `time`->`post` rename [M-082] (gates: section 4.1's equivalence/divergence/pooled-parity test triple) (shipped: tests/test_v4_merge_mpd.py; consumer ports incl. HonestDiD/PreTrendsPower calendar routes); (b) TripleDifference facade [M-013] + the SDDD alias [M-064] (shipped: tests/test_v4_merge_ddd.py; the engine relocation into the private shared mixin, the keyword-only staggered fit params, and the pscore_trim tightening [M-142]. The fit-time aggregate=/balance_e= carve-out rows are scheduled REMOVALS and so are cited in the phase-5 cell, not here - their only lifecycle version is removed_in 4.0); (c) CiC method= [M-015] + its results-field mirror [M-143] (shipped: tests/test_v4_merge_cic.py; method= is keyword-only and lowercase-only, the QDiD CLASS is deprecated while the METHOD is not, and ChangesInChangesResults.estimator -> .method carries a dual-key to_dict() window through 3.9) | | 4: release + soak | 3.9 cut | Migration guide written (skeleton: section 10); maintainer cuts 3.9; maint/3.8 rule active | -| 5: enforcement | 4.0 | Removals [M-010..M-015, M-020..M-027, M-139, M-030, M-032..M-047 old names, M-060, M-061, M-064, M-070..M-077, M-084, M-086..M-089, M-001..M-003, M-117, M-118, M-119, M-120, M-140, M-141, M-143] + the alias diet [M-132]..[M-134] + the amendment's old names [M-094] [M-095] [M-097..M-115] [M-136..M-138] [M-150] (incl. their consumer migrations and the `clean_control` serialized reporting key); M-031's old `time` name persists as the merged class's calendar column, so it is deliberately absent from the removal roster (its 4.0 enforcement is the M-085 behavior entry below); property window: [M-016] removed at 4.0 (item 9b; the planned 5.0 window was retired); storage flips [M-050..M-058] (their 4.0 -> 5.0 shims retired at 4.0, item 9b); default policies [M-004..M-006, M-128..M-131, M-080] + the shared cluster contract [M-155] (7b) and the SpilloverDiD df_convention knob [M-154] (7c); merged-class behavior enforcements [M-083] [M-085]; placebo-wrapper calendar contract [M-156] [M-157] (item 9b); warning retirement [M-007]; fastpath go/no-go [M-008]; diagnostic-family docs/roster reorganization [M-090]; sentinel retirement [M-093]; the family-wide anticipation validation [M-144], the family-wide pscore_trim validation [M-145], and the family-wide summary-alpha guard [M-146] (staggered family at the initial cut; extended to the non-staggered summaries post-cut), the LWDiD to_dataframe reshape [M-153] (behavior tightenings landing at 4.0 - shipped post-3.9-cut, terminal `done`, no removal/deprecation fields); the additive Wooldridge comparison-support policy [M-147] (`unsupported_period_action="drop"` preserves filtering, `"error"` refuses before period removal; same post-cut lifecycle); the inert SyntheticControl inner-budget pair [M-148] [M-149] (DeprecationWarning in 3.13.0, removed at 4.0 - item 9b); the LWDiD result-alias pair [M-151] [M-152] (removed at 4.0, item 9b); docs/llms.txt/README refresh | +| 5: enforcement | 4.0 | SHIPPED 2026-11-01 (items 1-9b + the release, item 10). Removals [M-010..M-015, M-020..M-027, M-139, M-030, M-032..M-047 old names, M-060, M-061, M-064, M-070..M-077, M-084, M-086..M-089, M-001..M-003, M-117, M-118, M-119, M-120, M-140, M-141, M-143] + the alias diet [M-132]..[M-134] + the amendment's old names [M-094] [M-095] [M-097..M-115] [M-136..M-138] [M-150] (incl. their consumer migrations and the `clean_control` serialized reporting key); M-031's old `time` name persists as the merged class's calendar column, so it is deliberately absent from the removal roster (its 4.0 enforcement is the M-085 behavior entry below); property window: [M-016] removed at 4.0 (item 9b; the planned 5.0 window was retired); storage flips [M-050..M-058] (their 4.0 -> 5.0 shims retired at 4.0, item 9b); default policies [M-004..M-006, M-128..M-131, M-080] + the shared cluster contract [M-155] (7b) and the SpilloverDiD df_convention knob [M-154] (7c); merged-class behavior enforcements [M-083] [M-085]; placebo-wrapper calendar contract [M-156] [M-157] (item 9b); warning retirement [M-007]; fastpath go/no-go [M-008]; diagnostic-family docs/roster reorganization [M-090]; sentinel retirement [M-093]; the family-wide anticipation validation [M-144], the family-wide pscore_trim validation [M-145], and the family-wide summary-alpha guard [M-146] (staggered family at the initial cut; extended to the non-staggered summaries post-cut), the LWDiD to_dataframe reshape [M-153] (behavior tightenings landing at 4.0 - shipped post-3.9-cut, terminal `done`, no removal/deprecation fields); the additive Wooldridge comparison-support policy [M-147] (`unsupported_period_action="drop"` preserves filtering, `"error"` refuses before period removal; same post-cut lifecycle); the inert SyntheticControl inner-budget pair [M-148] [M-149] (DeprecationWarning in 3.13.0, removed at 4.0 - item 9b); the LWDiD result-alias pair [M-151] [M-152] (removed at 4.0, item 9b); docs/llms.txt/README refresh | | 6: front door | 4.1 | `event_study(data, outcome, unit, time, first_treat, estimator=...)` comparison entry point over the staggered family (sketch only; specified in its own plan) | Citation semantic for the table: a cell may cite a row whose current `phase` @@ -1133,11 +1143,12 @@ cut's PR order; order and rationale only - the M-id rosters live in the phase-5 cell above and the lifecycle facts in the ledger. Every PR lands on main under the section 2 cut window with `__version__` unchanged; each flips its rows terminal and names them in its changelog fragment (3a excepted: a -tests-first PR that flips no rows). +tests-first PR that flips no rows). (Window closed by item 10, 2026-11-01.) -0. Spec/ledger refresh (the PR that wrote this subsection): the cut-window - rule, the roster/M-080/M-008 amendments, the [M-150] locator, the id - snapshot repair, and the `maint/3.13` branch cut. +0. Spec/ledger refresh (Shipped, #854 - the PR that wrote this subsection): the + cut-window rule, the roster/M-080/M-008 amendments, the [M-150] locator and + the id snapshot repair; the `maint/3.13` branch cut it also listed was NOT + performed (maintainer decision 2026-10-18). 1. New-estimator conformance - additive signatures plus ONE schema reshape: `LWDiDResults.summary(alpha=None)`, `to_dataframe(level=)` (the reshape, [M-153]), rows for its `pvalue`/`ci` aliases [M-151] [M-152] (lifecycle in @@ -1303,18 +1314,32 @@ tests-first PR that flips no rows). `tests/test_v4_storage_flip.py`, `tests/test_v4_merge_mpd.py`, `tests/test_methodology_synthetic_control.py`, `tests/test_lwdid.py` and `tests/test_diagnostics.py`. -10. Release: bump to 4.0.0 (closes the cut window), changelog compile, - migration-guide retitle, due gate green; re-execute (Rust backend) the +10. Release (SHIPPED 2026-11-01): `__version__` 4.0.0 closes the cut window; + `CHANGELOG.md` `[4.0.0]` compiled from the 26 fragments (the compiler's category + order now leads with Breaking Changes / Behavioral Changes); migration guide + and user guide flipped to present tense; the repo-wide deprecation grep re-run + (recorded in the 4.0-cut checklist below). Tutorials: of the eight the TODO row + listed, only 02_staggered_did and 09_real_world_examples carried committed + outputs, and both were re-executed on the Rust backend (movers exactly the 7a + p-values/CIs and the 7b `Std. errors: ... G=` lines; point estimates and SEs + unchanged); 01/04/05/07/11/13 have never carried outputs (nbmake-validated); + 16_wooldridge_etwfe was refreshed at 8b. Movers outside the list: + 27_cic_distributional_effects (the M-030 `post=` spelling in the practitioner + text) re-executed; 25_synthetic_control_policy re-executed - its committed + confidence-set / in-time-placebo outputs were a stale capture (the probe at its + own last commit, #842, already prints the refreshed numbers on the Python + backend, so no 4.0 commit moved them); 32_dml_did's Python-backend bootstrap + outputs left as captured (a backend-only difference, not a 4.0 mover). + Original scope statement, kept as history: re-execute (Rust backend) the tutorials whose clustered-default outputs moved at item 7a and were - deliberately left stale - 01_basic_did, 02_staggered_did, - 04_parallel_trends, 05_honest_did, 07_pretrends_power, - 09_real_world_examples, 11_imputation_did, 13_stacked_did, - 16_wooldridge_etwfe (NOT 16_survey_did: the survey df wins) - plus any - 8/9 movers; 24 is handled in 7a and 23 in 7b and re-validated in 7c (it - prints no p-values or CIs, so 7c moved no committed output). 7b's label changes also drift every committed tutorial - that renders an analytical DiD/TWFE summary (", G=" on the variance line) - or a SunAbraham summary (the new "Std. errors:" line, e.g. 02) - all - within the nine-notebook set above. + deliberately left stale - 01_basic_did, 02_staggered_did, 04_parallel_trends, + 05_honest_did, 07_pretrends_power, 09_real_world_examples, 11_imputation_did, + 13_stacked_did, 16_wooldridge_etwfe (NOT 16_survey_did: the survey df wins) - + plus any 8/9 movers; 24 is handled in 7a and 23 in 7b and re-validated in 7c + (it prints no p-values or CIs, so 7c moved no committed output). 7b's label + changes also drift every committed tutorial that renders an analytical + DiD/TWFE summary (", G=" on the variance line) or a SunAbraham summary (the + new "Std. errors:" line, e.g. 02) - all within the nine-notebook set above. Ordering constraints: 4a before 4b before 4c-i before 4c-ii; 4 before 5 and 6; 5 before 6 (the unified DDD container is born canonical); 7a before 7b before 7c; 7 and 8 @@ -1386,7 +1411,16 @@ major"/"flip") to catch anything born outside the matrix. RUN 2026-10-18 at v3.13.0 (4.0-cut framing): every FutureWarning/DeprecationWarning site (25 direct, 52 via the `_deprecation.py` helpers, 12 Sphinx `.. deprecated::` directives, the module `__getattr__`) resolves to a ledger row - nothing was -born outside the matrix in 3.10-3.13. Re-run once more in the release PR. +born outside the matrix in 3.10-3.13. RE-RUN 2026-11-01 at 4.0.0 (item 10, the +release PR): 0 warning-emitting FutureWarning/DeprecationWarning sites remain in +`diff_diff/` (the three textual hits are history comments), 0 warning-emitting +`_deprecation.py` helper call sites (the four shim helpers were deleted at 9b; +`require_arg`/`NOT_SUPPLIED` are required-arg plumbing, not deprecation), 0 Sphinx +`.. deprecated::` directives, no module `__getattr__` - every lifecycle fact is a +ledger row; the prose sweep (`docs/*.rst`, `docs/*.md`, README, CONTRIBUTING, +METHODOLOGY_REVIEW) found four stale present-tense sentences, all fixed in the +release PR (METHODOLOGY_REVIEW `lambda_reg`/`zeta` x2 and `min_*_periods`, +troubleshooting `time=` alias, REGISTRY M-082 alias note). ## 10. Migration-guide skeleton (`docs/migration-4.0.md`, written in Phase 4) @@ -1580,7 +1614,8 @@ enforcement is unchanged - at `cut_window` every scheduled row must be terminal, exactly as before. The release PR bumps `__version__` to `cut_window`, which closes the window; the keys stay as a historical record (`test_ledger_header_declares_cut_window` asserts `cut_base == __version__` -only while the window is open). +only while the window is open). (Closed 2026-11-01 by the 4.0.0 bump; the header +keys remain as the historical record.) **Cross-row migration rule.** Removing a symbol requires migrating, in the same diff, every other row whose locators or `code_refs` reference it (e.g. diff --git a/pyproject.toml b/pyproject.toml index 0b6269ff3..1cab7bab6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "maturin" [project] name = "diff-diff" -version = "3.13.0" +version = "4.0.0" description = "Difference-in-Differences causal inference with sklearn-like API. Callaway-Sant'Anna, Synthetic DiD, Honest DiD, event studies, parallel trends." readme = "README.md" license = "MIT" diff --git a/rust/Cargo.toml b/rust/Cargo.toml index 2c0c97724..d0a1a6070 100644 --- a/rust/Cargo.toml +++ b/rust/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "diff_diff_rust" -version = "3.13.0" +version = "4.0.0" edition = "2021" rust-version = "1.85" description = "Rust backend for diff-diff DiD library" diff --git a/tests/test_changelog_fragments.py b/tests/test_changelog_fragments.py index 539a05161..3b0860300 100644 --- a/tests/test_changelog_fragments.py +++ b/tests/test_changelog_fragments.py @@ -312,14 +312,26 @@ def test_category_order_and_contiguous_join(self, mod, tmp_path): "20260829-bbb.md": "### Fixed\n- fix from bbb\n\n### Added\n- add from bbb\n", "20260828-aaa.md": "### Fixed\n- fix from aaa\n", "20260830-ccc.md": "### Internal\n- internal from ccc\n", + # Declared in reverse compiler order on purpose: the compiled + # placement must come from CATEGORIES, not fragment order. + "20260827-ddd.md": ( + "### Internal\n- internal from ddd\n\n" + "### Behavioral Changes\n- behavior from ddd\n\n" + "### Breaking Changes\n- breaking from ddd\n" + ), }, ) assert run_compile(mod, root) == 0 text = (root / "CHANGELOG.md").read_text() section = text[text.index("## [1.3.0]") : text.index("## [1.2.0]")] - # Category order: Added before Fixed before Internal. + # Category order: the two breaking categories lead (a major release + # reads breakage first), then Added before Fixed before Internal. assert ( - section.index("### Added") < section.index("### Fixed") < section.index("### Internal") + section.index("### Breaking Changes") + < section.index("### Behavioral Changes") + < section.index("### Added") + < section.index("### Fixed") + < section.index("### Internal") ) # Within Fixed: ascending filename order, joined contiguously. assert "- fix from aaa\n- fix from bbb" in section