feat: add LWDiD estimator (Lee & Wooldridge 2025, 2026)

[gorgeousfish]

This PR adds native support for the Lee & Wooldridge (2025, 2026) rolling-transformation difference-in-differences method. The approach works by applying unit-specific time-series transformations (demeaning or detrending) to panel outcomes before treatment, converting the panel DiD problem into a standard cross-sectional one. Once transformed, any treatment-effect estimator — regression adjustment, inverse probability weighting, doubly robust, or propensity-score matching — can be applied directly to the cross-section. The method handles both common-timing and staggered-adoption designs with flexible control-group selection.

The implementation lives entirely within diff_diff/lwdid*.py (9 modules) and introduces zero new runtime dependencies — it reuses the existing solve_ols, solve_logit, and safe_inference infrastructure. The IPW and IPWRA standard errors use the full semiparametric influence function with propensity-score and outcome-model correction terms, matching the variance formula in the authors' Stata package.

Numerical correctness has been validated against the reference lwdid Python package across all supported configurations. The RA path achieves machine-precision agreement (≤1e-10), and IPW/IPWRA paths agree to within 1%. The California Proposition 99 results from Table 3 of LW (2026) are reproduced exactly: demeaning ATT = −0.422, detrending ATT = −0.227.

Beyond core estimation, the PR includes wild cluster bootstrap inference (Rademacher/Mammen/Webb), Fisher randomization inference, parallel-trends pre-testing, sensitivity analysis, clustering-level diagnostics, and visualization methods. A tutorial notebook walks through the full workflow on the papers' empirical datasets (California smoking data, Castle Doctrine laws, Walmart county-level entry).

Methodology references (required if estimator / math changes)

• Method name(s): LWDiD (Lee & Wooldridge rolling-transformation DiD)
• Paper / source link(s):
  • Lee, S. J. & Wooldridge, J. M. (2025). https://ssrn.com/abstract=4516518
  • Lee, S. J. & Wooldridge, J. M. (2026). https://ssrn.com/abstract=5325686
• Any intentional deviations from the source (and why): None. Implementation follows Procedures 2.1, 3.1, and 4.1 exactly. IPW/IPWRA SE uses the full semiparametric influence function matching the authors' Stata package (lwdid.ado).

Validation

• Tests added/updated: 9 test files, 222 tests (unit tests, numerical precision, equivalence against lwdid-py, wild bootstrap, randomization, diagnostics, sensitivity, visualization)
• Backtest / simulation / notebook evidence (if applicable): Tutorial notebook (docs/tutorials/26_lwdid.ipynb) reproduces Tables 3–4 from LW (2026) on California Proposition 99 data. ATT values match published results to 0.04% precision.

Security / privacy

• Confirm no secrets/PII in this PR: Confirmed. No secrets, tokens, or PII. Datasets included (smoking.csv, walmart.csv, castle.csv) are publicly available research data from the referenced papers.

[igerber]

@gorgeousfish Thanks for this - it's a serious, well-prepared contribution, and the method is a great
fit for the library. I'd like to move it forward.

That said, before I do a full review, one thing to clear first: licensing. This implementation
appears closely related to your lwdid-py package, which is AGPL-3.0, while diff-diff
is MIT. Could you confirm:

1. You hold the copyright to all code in this PR (i.e., it's your own work, not derived
   from third-party code such as the authors' Stata lwdid package), and you're
   contributing it under diff-diff's MIT license.
2. The bundled datasets (smoking.csv, castle.csv, walmart.csv, .dta files) are
   redistributable - a pointer to their original source/terms would help.

[gorgeousfish]

Thanks for the quick response and the positive signal.

On licensing:

Yes, I hold the copyright to all code in this PR. It's my own independent implementation, not derived from Lee & Wooldridge's Stata lwdid package or any third-party source. I'm contributing it under diff-diff's MIT license.

On datasets:

smoking.csv: Abadie, Diamond & Hainmueller (2010), California tobacco control program. Publicly available, widely redistributed in academic packages.

castle.csv: Cheng & Hoekstra (2013) / Cunningham (2021), Castle Doctrine laws. Publicly available.

walmart.csv: county-level panel from Brown & Butts (2025, Journal of Econometrics), constructed from County Business Patterns (CBP) data. Publicly available government statistical data.