Release 3.3.2: dCDH by_path × trends extensions, Yatchew mean_independence, HAD Phase 4 R-parity

CHANGELOG.md

@@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [Unreleased]
+## [3.3.2] - 2026-04-26
 
 ### Added
 - **`ChaisemartinDHaultfoeuille.by_path` is now compatible with `trends_linear` (DID^{fd} group-specific linear trends) and `trends_nonparam` (state-set trends).** For `trends_linear`, the first-differencing transform runs once globally before path enumeration, so per-path raw second-differences `DID^{fd}_{path, l}` surface on `path_effects[path]["horizons"][l]` automatically. Per-path **cumulated level effects** `delta_{path, l} = sum_{l'=1..l} DID^{fd}_{path, l'}` (the quantity R returns under `did_multiplegt_dyn(..., by_path, trends_lin)`) surface on the new `results.path_cumulated_event_study[path][l]` field, mirroring the global `linear_trends_effects` cumulation. `to_dataframe(level="by_path")` exposes `cumulated_effect` / `cumulated_se` columns (always present, NaN-when-None — mirrors the `cband_*` convention from PR #374); `summary()` renders a "Cumulated Level Effects (DID^{fd}, trends_linear)" sub-section under each per-path block. SE on the cumulated layer is the conservative upper bound (sum of per-horizon component SEs, NaN-consistent), matching the global `linear_trends_effects` convention. Path enumeration runs on the post-first-differenced `N_mat_fd`: switchers with `F_g==2` fail the window-eligibility check and are dropped from path enumeration entirely (the existing global `F_g >= 3` warning still surfaces the issue), so a path whose switchers all have `F_g < 3` is silently absent from `path_effects` rather than present-with-NaN. Placebo under `trends_linear` returns RAW per-horizon values — there is no per-path placebo cumulation surface in either Python or R. For `trends_nonparam`, the set membership column is validated and stored once globally as `set_ids_arr`; the `set_ids` parameter is now threaded through the four per-path IF helpers (`_compute_path_effects`, `_compute_path_placebos`, `_collect_path_bootstrap_inputs`, `_collect_path_placebo_bootstrap_inputs`) so per-path analytical SE, bootstrap, placebos, and sup-t bands all consume the set-restricted control pool automatically. Per-period effects remain unadjusted under both extensions, consistent with the existing per-period DID contract. Validated against R via two new golden-value scenarios: `single_baseline_multi_path_by_path_trends_lin` (n_periods=13, F_g >= 4, cohort-single-path; per-path cumulated point estimates match R bit-exactly with `POINT_RTOL=1e-9`, cumulated SE within `CUM_SE_RTOL=0.20`) and `multi_path_reversible_by_path_trends_nonparam` (per-path point estimates AND placebos match R bit-exactly with `POINT_RTOL=1e-9`, per-path SE within `SE_RTOL=0.15`). **F_g=3 boundary-case divergence (`by_path + trends_linear`):** `F_g=3` switchers have only 1 valid pre-window Z value after first-differencing, triggering 30%+ relative divergence between Python and R per-path point estimates on paths whose switchers include `F_g=3`. A targeted `UserWarning` fires at fit-time on this regime; R parity is asserted only on the `F_g >= 4` parity fixture. Placebo parity for `trends_linear` is intentionally skipped (R's per-path placebo computation re-runs on the path-restricted subsample with different control eligibility than Python's global-then-disaggregate architecture surfaces; placebo + `trends_linear` is exercised via internal regression only). Cross-path cohort-sharing SE deviation from R documented for `path_effects` is inherited unchanged. Gates at `chaisemartin_dhaultfoeuille.py:1014-1023` removed; `by_path` docstring updated to add the two new compatibility paragraphs and remove `trends_linear` / `trends_nonparam` from the incompatible list. R-parity tests at `tests/test_chaisemartin_dhaultfoeuille_parity.py::TestDCDHDynRParityByPathTrendsLinear` and `::TestDCDHDynRParityByPathTrendsNonparam`; cross-surface regressions at `tests/test_chaisemartin_dhaultfoeuille.py::TestByPathTrendsLinear` and `::TestByPathTrendsNonparam`. See `docs/methodology/REGISTRY.md` §ChaisemartinDHaultfoeuille `Note (Phase 3 by_path ...)` → "Per-path linear-trends DID^{fd}" and "Per-path state-set trends" for the full contract.
@@ -1421,6 +1421,7 @@ 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
+[3.3.2]: https://github.com/igerber/diff-diff/compare/v3.3.1...v3.3.2
 [3.3.1]: https://github.com/igerber/diff-diff/compare/v3.3.0...v3.3.1
 [3.3.0]: https://github.com/igerber/diff-diff/compare/v3.2.0...v3.3.0
 [3.2.0]: https://github.com/igerber/diff-diff/compare/v3.1.3...v3.2.0

CITATION.cff

@@ -7,8 +7,8 @@ authors:
     family-names: Gerber
     orcid: "https://orcid.org/0009-0009-3275-5591"
 license: MIT
-version: "3.3.1"
-date-released: "2026-04-25"
+version: "3.3.2"
+date-released: "2026-04-26"
 doi: "10.5281/zenodo.19646175"
 url: "https://github.com/igerber/diff-diff"
 repository-code: "https://github.com/igerber/diff-diff"

diff_diff/__init__.py

@@ -287,7 +287,7 @@
 DCDH = ChaisemartinDHaultfoeuille
 HAD = HeterogeneousAdoptionDiD
 
-__version__ = "3.3.1"
+__version__ = "3.3.2"
 __all__ = [
     # Estimators
     "DifferenceInDifferences",

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: 3.3.1
+- Version: 3.3.2
 - Repository: https://github.com/igerber/diff-diff
 - License: MIT
 - Dependencies: numpy, pandas, scipy (no statsmodels dependency)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "maturin"
 
 [project]
 name = "diff-diff"
-version = "3.3.1"
+version = "3.3.2"
 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"

rust/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "diff_diff_rust"
-version = "3.3.1"
+version = "3.3.2"
 edition = "2021"
 rust-version = "1.85"
 description = "Rust backend for diff-diff DiD library"