mirror(#812): twfeweights port - CI review only, do not merge

README.md

@@ -132,6 +132,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`.
 - [Manipulation Testing](https://diff-diff.readthedocs.io/en/stable/api/regression_discontinuity.html) - Cattaneo, Jansson & Ma (2020) density-discontinuity test (`RDDensityTest`): rddensity 3.0 parity, robust bias-corrected inference, unrestricted/restricted models, mass-point adjustment
 - [Parallel Trends Testing](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html) - simple and Wasserstein-robust parallel trends tests, equivalence testing (TOST)
 - [Placebo Tests](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html) - placebo timing, group, permutation, leave-one-out
+- [TWFE Weight Diagnostics](https://diff-diff.readthedocs.io/en/stable/api/twfe_weights.html) - Baker et al. (2025) implicit weights a TWFE regression places on each ATT(g,t), against the ATT^O / ATT^simple targets, with the pre-period contribution. Ports Callaway's `twfeweights` (MIT)
 - [Honest DiD](https://diff-diff.readthedocs.io/en/stable/api/honest_did.html) - Rambachan & Roth (2023) sensitivity analysis: robust CI under PT violations, breakdown values
 - [Pre-Trends Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/pretrends.html) - Roth (2022) minimum detectable violation and power curves
 - [Power Analysis](https://diff-diff.readthedocs.io/en/stable/api/power.html) - analytical and simulation-based MDE, sample size, power curves for study design

TODO.md

@@ -21,6 +21,7 @@ Related tracking surfaces:
 
 | Issue | Location | Origin | Effort | Priority |
 |-------|----------|--------|--------|----------|
+| `decompose_twfe_weights(method="aipw")`: port upstream `implicit_aipw_weights` (covariate-adjusted only; `twfe_weights.py` rejects the value today and lists `"fwl"`). The R goldens already carry the `decompose.aipw` / `balance.aipw` / `two_period.*` blocks under `meta.reserved_blocks`, so no R re-run is needed | `diff_diff/twfe_weights.py` | #812 | Mid | Medium |
 | Consolidate the remaining estimator-entangled DR/logit score variants (`staggered.py::_doubly_robust` + RC twins, `triple_diff.py`, `lwdid.py`, `wooldridge.py`) onto the shared `_dr_scores.py` module, each migration with its own committed oracle capture (the ContinuousDiD lift's two-tier pattern in `tests/test_dr_scores.py`); and add a ridge vcov path to `solve_ridge` if an estimator ever needs analytical ridge inference | `diff_diff/_dr_scores.py` | dml-b0 | Mid | Low |
 | Numeric between-period cohorts (e.g. `first_treat=4.5` with integer times) are rejected by LWDiD while CallawaySantAnna estimates them and LWDiD's own datetime/Period cohorts map to the next observed period — close the dtype asymmetry by adopting the next-observed-period mapping for numeric cohorts too (contract documented in REGISTRY cohort-encodings Note + `docs/api/lwdid.rst` Input Contract). Lands only after PR #588 merges | `diff_diff/lwdid.py` | #588 | Quick | Low |
 | Implement the LW 2026 eq. 7.9/7.10 unit-average cohort estimand (regress per-unit post-average transformed outcomes on `[1, D_g]` vs never-treated) as an alternative to the documented cell-mass `cohort_effects` convention (REGISTRY within-cohort aggregation Note; the two differ on unbalanced panels, where cell-mass weights units by observed post periods). Needs the 7.10 regression + its covariance on the NT path. Lands only after PR #588 merges | `diff_diff/lwdid_staggered.py` | #588 | Quick | Low |
@@ -83,6 +84,7 @@ generic sparse-FE, QR+SVD rank-detection redundancy, `check_finite` bypass — m
 
 | Issue | Location | Origin | Effort | Priority |
 |-------|----------|--------|--------|----------|
+| TWFE-weights tutorial section: `attgt_weights` (all three `type=` values side by side) + `decompose_twfe_weights` + `plot_twfe_weights` on `mpdta`, either as a section of `02_staggered_did.ipynb` or a new notebook registered in `docs/tutorials/index.rst`; follow-on PR by decision, the feature ships without it | `docs/tutorials/02_staggered_did.ipynb` | #812 | Mid | Medium |
 | Geo-experiments tutorial: the SDiD fits on the `generate_factor_data(...)` panel (conversions ~1,500, sigma-hat ~310) hit the Frank-Wolfe iteration cap - the placebo fit warns once and the bootstrap cross-check warns on 100/100 draws - yet the narrative presents the bootstrap SE as a paper-faithful robustness check. Pre-existing on `main` under BOTH backends (surfaced when the notebook outputs were refreshed for the shape-only fit diagnostic); the committed April outputs predate the aggregated non-convergence warning. Either rescale / re-tune the DGP (or raise `max_iter` / loosen `min_decrease`) so the refits converge and re-execute, or label the bootstrap result unreliable and drop it from the "Validity evidence" claim. | `docs/tutorials/18_geo_experiments.ipynb` | local codex review of the SDiD shape-only fit-RMSE PR | Mid | Low |
 | Replicate Chang (2020) §4.2.1 ML-design RCS DGP — requires a penalized (Lasso-class) propensity learner or a maintainer fixture via the duck-typed `propensity_learner=` object route; native logit+linear verified pure noise at (N=500, p=100) across ~20 review seeds (att ~ −23..+16, SE ~4-10, EPV ~1.4-1.6 vs threshold 10, fitted out-of-fold clipping ~5-29%, mean ~14%); at (N=200, p=100) `outcome_learner="linear"` fails closed on control-fold rank deficiency even under an oracle propensity (use ridge/sieve there); §4.2.2 is replicated | `tests/test_methodology_dml_did.py`, `diff_diff/_learners.py` | DML PR-B2 | Mid | Low |
 | Optional scheduled end-to-end execution gate for the MMM tutorials (29/30): a cron-only workflow (or extension of `mmm-interop.yml`) that executes both notebooks in isolated exact-pin environments, so a stale/invalid committed posterior cannot stay green indefinitely - today the hybrid posture (deliberate: notebooks execute locally with committed outputs; CI smoke-tests the exporters without sampling; drift tests pin source + committed-output needles) leaves the MCMC claims un-re-executed in CI | `.github/workflows/mmm-interop.yml`, `docs/tutorials/29_mmm_calibration_pymc.ipynb`, `docs/tutorials/30_mmm_calibration_meridian.ipynb` | mmm-interop | Mid | Low |