Update REGISTRY.md with verified DifferenceInDifferences behavior

[igerber]

Summary

• Fix empty cell edge case documentation: empty cells cause rank deficiency (handled per rank_deficient_action), not ValueError as previously documented
• Mark DifferenceInDifferences requirements checklist as verified
• Add comprehensive methodology tests for DifferenceInDifferences estimator
• Update methodology review document with verification findings

Methodology references (required if estimator / math changes)

• Method name(s): DifferenceInDifferences (basic 2x2 DiD)
• Paper / source link(s): N/A - documentation correction only, no methodology changes
• Any intentional deviations from the source (and why): None - this PR corrects the documentation to match actual (correct) R-style behavior

Validation

• Tests added/updated: tests/test_methodology_did.py (new comprehensive methodology test suite)
• Backtest / simulation / notebook evidence (if applicable): N/A

Security / privacy

• Confirm no secrets/PII in this PR: Yes

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Generated with Claude Code

[github-actions]

Overall Assessment

• ⚠️ Needs changes

Executive Summary

• P1: Singleton-cluster handling is documented as “dropped with warning” and marked verified, but the implementation doesn’t drop or warn and the new tests don’t assert that behavior.
• P3: The empty-cell test docstring still quotes the old “ValueError” behavior after the registry update.
• No estimator code changes; the added methodology tests otherwise align with the DiD formula and R comparisons.

Methodology

• Severity: P1 | Impact: The registry states singleton clusters are dropped with a warning, and the review now marks that edge case as verified, but the cluster-robust vcov path keeps all clusters with no warning and the new singleton test only asserts “no error.” This is a methodology/documentation mismatch that can mislead users about SE handling for singleton clusters. | Concrete fix: Either implement singleton-cluster drop + warning in diff_diff/linalg.py and assert it in tests, or update the registry and review notes to reflect the current behavior. References: docs/methodology/REGISTRY.md:63, diff_diff/linalg.py:783, tests/test_methodology_did.py:926, METHODOLOGY_REVIEW.md:67, METHODOLOGY_REVIEW.md:88

Code Quality

• No findings.

Performance

• No findings.

Maintainability

• No findings.

Tech Debt

• No findings.

Security

• No findings.

Documentation/Tests

• Severity: P3 | Impact: The empty-cell test docstring still quotes the old registry behavior (“raise ValueError”) after the registry change, which can confuse maintenance and future reviewers. | Concrete fix: Update the docstring to reflect rank-deficiency handling or remove the outdated quote. Reference: tests/test_methodology_did.py:894

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 7713bb2ee53fb2aaeacf3bd75123ae80977b93f6

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Overall assessment: ⚠️ Needs changes

Executive Summary

• REGISTRY’s singleton-cluster variance note conflicts with the implemented cluster‑robust formula for DifferenceInDifferences.
• The new absorb R² test compares within‑R² to overall R²; this expectation is not valid and may be flaky.
• significance_stars test omits the “.” case even though the API can return it.
• R benchmark tests spin up multiple R processes; if R is installed, this can slow CI noticeably.

Methodology

• P1 Impact: The REGISTRY now states singleton clusters “contribute 0 to cluster variance,” but the implementation aggregates cluster scores for all clusters (including singletons), so the contribution is generally non‑zero; this misstates the variance behavior for DifferenceInDifferences. Fix: update the REGISTRY text to describe the current sandwich estimator (singletons included with no special handling), or change the computation if you intend to drop or zero‑weight singletons. docs/methodology/REGISTRY.md:L58-L64, diff_diff/linalg.py:L790-L813

Code Quality

• No issues found.

Performance

• P3 Impact: Each R comparison test launches a fresh Rscript, which can add significant wall time when R is available. Fix: cache R results in a session-scoped fixture or consolidate into a single R invocation per dataset. tests/test_methodology_did.py:L373-L643

Maintainability

• No issues found.

Tech Debt

• No issues found.

Security

• No issues found.

Documentation/Tests

• P2 Impact: test_absorb_improves_r_squared compares R² from a within‑transformed model (absorb) against the untransformed model; these are not comparable metrics and the assertion can be misleading or flaky. Fix: compare like‑for‑like (e.g., compute within‑R² for both) or drop the monotonicity expectation and just assert r_squared is finite. tests/test_methodology_did.py:L1243-L1266, diff_diff/estimators.py:L231-L316
• P3 Impact: test_significance_stars_property disallows “.” even though the public API returns it for 0.05 < p < 0.1; this could fail if the test data changes. Fix: include “.” in the accepted set or compare against _get_significance_stars output. tests/test_methodology_did.py:L1512-L1519, diff_diff/results.py:L183-L199

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 820dd783cfbacf2cbc23436d2603491f06314a2e

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Methodology

• P1 — Impact: DifferenceInDifferences registry now says singleton clusters contribute 0 to cluster-robust variance, but the implementation sums per-cluster scores so singletons contribute non-zero terms; this misdocuments variance/SE behavior and conflicts with the new singleton-cluster test. Fix: update the registry wording to match the sandwich estimator contribution for singleton clusters (or change the implementation to drop/zero them if that is intended). refs: docs/methodology/REGISTRY.md:63, diff_diff/linalg.py:803, tests/test_linalg.py:451

Code Quality

• No issues found.

Performance

• No issues found.

Maintainability

• No issues found.

Tech Debt

• No issues found.

Security

• No issues found.

Documentation/Tests

• No issues found.

Overall Assessment

• ⚠️ Needs changes

Executive Summary

• Singleton-cluster variance behavior is misdocumented versus implementation/tests.
• No estimator logic changes; updates are documentation and DiD methodology tests.
• R benchmarks remain optional and gated by fixest availability.

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: ab5add0dae081a0a1066f5bd0383593c0a851408

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Overall Assessment: ✅ Looks good

Executive Summary

• Registry updates for DiD edge cases align with current rank‑deficiency handling and cluster‑robust computation; no methodology deviations detected.
• The new DiD methodology suite is broad (ATT formula, R benchmarks, edge cases, FE equivalence).
• Performance risk: repeated Rscript invocations and 99–199 bootstrap iterations per test are unmarked slow and may lengthen CI when R is available.
• Minor doc clarity: singleton cluster variance wording could be more explicit about per‑cluster score outer products.

Methodology

• None found; updates in docs/methodology/REGISTRY.md match current behavior.

Code Quality

• None found.

Performance

• P2 | Impact: Multiple Rscript calls per dataset plus repeated wild‑bootstrap runs can materially increase CI time when R is installed. | Fix: cache R results with session‑scoped fixtures and/or mark these tests @pytest.mark.slow (or reduce bootstrap iterations for tests). (tests/test_methodology_did.py:L373-L653, tests/test_methodology_did.py:L787-L883)

Maintainability

• None found.

Tech Debt

• None found.

Security

• None found.

Documentation/Tests

• P3 | Impact: The singleton‑cluster description could be misread as using global X'X instead of per‑cluster score outer products, which may confuse readers. | Fix: clarify the formula to u_i^2 X_i X_i' (or score_g score_g' for n_g=1). (docs/methodology/REGISTRY.md:L58-L64)