Rewrite TripleDifference to match R's triplediff::ddd()

[igerber]

Summary

• Rewrite all 3 estimation methods (DR, IPW, RA) from naive cell-mean approach to three-DiD decomposition matching R's triplediff::ddd() (Ortiz-Villavicencio & Sant'Anna 2025)
• Achieve <0.001% relative difference from R for both ATT and SE across all methods, with/without covariates, all 4 DGP types (24 comparisons)
• Add 91 methodology verification tests covering hand calculations, pre-computed R comparison, live R comparison, edge cases, and scale validation
• Add R and Python benchmark scripts for triple difference
• Update METHODOLOGY_REVIEW.md (status: Complete) and REGISTRY.md (reference impl, SE formula, cross-ref table)

Methodology references (required if estimator / math changes)

• Method name(s): Triple Difference (DDD) — regression adjustment, IPW, doubly robust
• Paper / source link(s): Ortiz-Villavicencio, M., & Sant'Anna, P.H.C. (2025). Better Understanding Triple Differences Estimators. arXiv:2505.09942
• R reference implementation: triplediff::ddd() v0.2.1 (CRAN) — official companion by paper authors
• Any intentional deviations from the source: Uses panel=FALSE (repeated cross-section) mode only; panel mode not yet implemented

Validation

• Tests added/updated:
  • tests/test_methodology_triple_diff.py (91 new tests)
  • tests/test_triple_diff.py (5 expectations updated)
• All 141 tests pass (91 methodology + 50 existing)
• R comparison: ATT <0.001% for all 24 comparisons (4 DGPs × 3 methods × 2 cov settings)
• R comparison: SE <0.001% for all 24 comparisons

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

• TripleDifference (RA/IPW/DR) was rewritten to a three‑DiD + IF SE approach; the high‑level structure matches the registry, but three methodology compliance gaps remain.
• robust/cluster parameters are now no-ops; SEs are always unclustered IF, which can materially understate uncertainty.
• Rank-deficiency handling no longer matches the documented contract (warn/silent), leaving collinearity undetected.
• Registry-required warnings for low cell counts are missing.
• Documentation still advertises robust/cluster SEs; tests don’t cover the warn path or clustering behavior.

Methodology

• Severity: P1 | Impact: robust and cluster are accepted and documented, but SEs are always computed from unclustered influence functions across RA/IPW/DR, so clustering is silently ignored and uncertainty can be understated. | Fix: implement robust/cluster variance of IF contributions (e.g., cluster-robust variance of IF sums) or explicitly deprecate/raise for these params and update docs/registry accordingly. (diff_diff/triple_diff.py:L350-L560, diff_diff/triple_diff.py:L741-L885)
• Severity: P1 | Impact: Rank-deficient handling no longer matches the contract/registry: "warn"/"silent" are documented to detect/drop collinearity, but _fit_predict_mu only errors and otherwise silently uses np.linalg.lstsq, so collinear covariates can change estimates without warning (affects RA/DR). | Fix: restore rank-deficiency detection for "warn" (emit warning + drop dependent columns) and "silent" (drop without warning), or reuse the prior solve_ols path with rank_deficient_action. (diff_diff/triple_diff.py:L359-L363, diff_diff/triple_diff.py:L937-L949, docs/methodology/REGISTRY.md:L808-L812)
• Severity: P1 | Impact: Registry requires warnings for low cell counts, but validation only errors on empty cells; sparse cells pass silently, risking unstable DDD/IF variance without user warning. | Fix: add a min_cell_count threshold (or warn below a fixed default) for all 8 cells and each {j,4} pairwise subset. (docs/methodology/REGISTRY.md:L776-L779, diff_diff/triple_diff.py:L626-L647)

Code Quality
No findings.

Performance
No findings.

Maintainability
No findings.

Tech Debt
No findings.

Security
No findings.

Documentation/Tests

• Severity: P2 | Impact: Module/class docs still claim robust/cluster SEs and “Current Implementation (v1.3)” details that no longer match IF‑only SEs, which will mislead users. | Fix: update docstrings (and any user-facing docs) to reflect IF SEs and the actual inference options. (diff_diff/triple_diff.py:L14-L18, diff_diff/triple_diff.py:L350-L353)
• Severity: P3 | Impact: Tests cover rank_deficient_action="error" and "silent" but not "warn", and there are no tests for robust/cluster inference behavior, so regressions here are unguarded. | Fix: add a "warn" test that asserts a warning and add tests for cluster/robust variance once implemented. (tests/test_triple_diff.py:L891-L936)

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 6b84ec766c4d12ded1cfe3f48bb162a3a117d4c5

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Overall assessment: ⚠️ Needs changes

Executive Summary

• TripleDifference (RA/IPW/DR) is now implemented as the three‑DiD decomposition with IF‑based SEs and aligns with the Registry formulas; the added R-comparison tests are strong.
• P1: Cluster‑robust SE divides by (G-1) without guarding G<2, yielding undefined variance for single‑cluster runs.
• P1: The Registry lists propensity‑score overlap as an assumption check for IPW/DR, but the code only clips pscores without warning when overlap is weak.
• P2: Missing‑value handling for cluster IDs can silently drop IF contributions while reporting a different cluster count.
• Add targeted tests for cluster edge cases and overlap diagnostics.

Methodology

• P1: Cluster‑robust SE is undefined for fewer than 2 clusters; the current formula divides by (G-1) without a guard. Impact: se becomes inf (and inference NaN) for single‑cluster runs, which is not a valid variance estimate. Fix: explicitly check n_clusters_val < 2 and raise ValueError or set se=np.nan with a warning before the division, consistent with other cluster‑robust paths. Location: diff_diff/triple_diff.py:L899-L910.
• P1: Registry lists “Propensity score overlap required for IPW/DR methods” under assumption checks, but the implementation only clips pscores and provides diagnostics; it does not warn or fail when overlap is poor. Impact: users can receive biased IPW/DR estimates without any signal in low‑overlap subsets. Fix: detect when pscore_sub hits trim bounds (or when effective overlap is low per subset) and warn or error, and surface this in results. Locations: docs/methodology/REGISTRY.md:L776-L779, diff_diff/triple_diff.py:L818-L834.

Code Quality

• P2: cluster IDs are not validated for missing values; np.unique includes NaN but self._cluster_ids == NaN is always false, so those observations are excluded from cluster sums, while n_clusters uses nunique() (drops NaN). Impact: understated variance and inconsistent reported cluster counts. Fix: treat cluster as required when provided and error on missing values (or drop with warning), and compute n_clusters from the same unique set used for SE. Locations: diff_diff/triple_diff.py:L520-L565, diff_diff/triple_diff.py:L899-L905.

Performance

• No issues found.

Maintainability

• No issues found.

Tech Debt

• No issues found.

Security

• No issues found.

Documentation/Tests

• P2: No coverage for cluster edge cases (single cluster, missing cluster IDs) and no test for overlap‑diagnostic behavior. Impact: regressions in cluster SE handling or assumption checks may slip. Fix: add tests in tests/test_methodology_triple_diff.py or tests/test_triple_diff.py for cluster with 1 cluster, with NaN IDs, and for overlap warnings if implemented.

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: af777ffc4ad9a4779cd8e717857ed27bf8bdbb7c

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Overall Assessment: ⚠️ Needs changes

Executive Summary

• P1: Propensity-score failure fallback still applies PS correction/Hessian, which can misstate IF-based SEs when the PS model fails. diff_diff/triple_diff.py:L830-L857
• P2: r_squared computation ignores rank_deficient_action and always uses "silent", which is inconsistent with estimator settings. diff_diff/triple_diff.py:L965-L979
• Tests not run (review based on diff + registry/docstrings only).

Methodology

• P1 | Impact: If _logistic_regression fails, the code falls back to constant pscore_sub but still computes a Hessian and applies PS correction in the IF. This mixes a non-estimated PS with an “estimated PS” correction and can distort SEs, violating the intended IF-based variance behavior in the Registry when PS estimation fails (especially likely in small pairwise subsets with separation). Concrete fix: When PS estimation fails, set hessian=None and skip PS correction, and emit a warning. Alternatively fit an intercept-only PS model and compute a consistent Hessian for that fallback. diff_diff/triple_diff.py:L830-L857

Code Quality

• P2 | Impact: r_squared uses solve_ols(..., rank_deficient_action="silent") regardless of user-specified rank_deficient_action. This is inconsistent with the estimator behavior and could hide rank-deficiency issues in diagnostics. Concrete fix: Use self.rank_deficient_action here, or set r_squared=None if rank-deficient and rank_deficient_action=="error". diff_diff/triple_diff.py:L965-L979

Performance

• No findings.

Maintainability

• No findings.

Tech Debt

• No findings.

Security

• No findings.

Documentation/Tests

• No findings (new methodology and R-comparison coverage looks thorough; not executed in this review).

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: ba50dd179e14361931ec58c632e65f233353494a

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Overall assessment: ⚠️ Needs changes

Executive summary

• Affected methods: TripleDifference RA/IPW/DR are fully rewritten to three‑DiD decomposition with IF-based SEs; registry and methodology review are updated and extensive tests/benchmarks added.
• Methodology cross‑check against docs/methodology/REGISTRY.md and in‑code docstrings shows alignment on decomposition, IF SE, overlap warnings, and clustering.
• Issue: non‑finite influence-function values are silently zeroed, which can mask invalid inference and violate the registry’s NaN‑propagation rule.
• Minor doc mismatch: the convenience wrapper still claims robust toggles SEs despite being a no‑op now.

Methodology

• P1: Impact: inf_j non‑finite values are replaced with 0.0, which can yield finite SE/t‑stats when the IF is undefined, violating the registry’s “NaN inference for undefined statistics” edge‑case guarantee. Concrete fix: propagate non‑finite IFs (e.g., if any ~np.isfinite(inf_j), set se=np.nan and let safe_inference produce NaNs, or raise/warn and skip inference). Location: diff_diff/triple_diff.py:L903-L912.
• Note: Other registry requirements (3‑DiD decomposition, IF SE formula, overlap warning, cluster SE) appear consistent.

Code Quality
No findings.

Performance
No findings.

Maintainability
No findings.

Tech Debt
No findings.

Security
No findings.

Documentation/Tests

• P3: Impact: triple_difference() docstring still says robust controls robust SEs, but it is now a no‑op per class docs/registry; this can mislead users. Concrete fix: update the wrapper docstring to match the class parameter note (“IF‑based SEs are inherently robust; robust retained for API compatibility”). Location: diff_diff/triple_diff.py:L1535-L1536.

Suggestions (optional)

1. Update IF NaN handling as above and add a small test that forces non‑finite IF (e.g., by monkeypatching _compute_did_rc_* to return NaN) to verify NaN propagation through inference.

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 5d047482afcb65ed7261bed90fedf00109367b76

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Overall assessment: ✅ Looks good

Executive Summary

• Affected methods: TripleDifference RA/IPW/DR; the rewrite to three‑DiD decomposition with IF‑based SEs aligns with docs/methodology/REGISTRY.md and module docstrings.
• Assumption/edge handling now matches the registry (8‑cell validation, low‑cell warnings, overlap warnings, NaN‑safe inference, cluster SE).
• Extensive methodology and R‑comparison tests were added; I did not run them in this review.

Methodology

• No issues found; implementation matches the Registry formulas and stated assumptions for TripleDifference (RA/IPW/DR).

Code Quality

• Severity P2 | Impact: If cluster is set but the column is missing, data[self.cluster] raises a KeyError before validation, which is inconsistent with other input errors. | Concrete fix: Add cluster to required_cols in _validate_data when self.cluster is provided, or explicitly guard data[self.cluster] before access. diff_diff/triple_diff.py:520-569

Performance

• No issues found.

Maintainability

• No issues found.

Tech Debt

• No issues found.

Security

• No issues found.

Documentation/Tests

• Severity P3 | Impact: Test status unknown in this review. | Concrete fix: Run the new and existing tests (e.g., pytest tests/test_methodology_triple_diff.py tests/test_triple_diff.py) to confirm.

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 1614459aee62b6bc74c2e078bfc6bfca9f668563

---

Overall assessment: ⚠️ Needs changes

Executive summary

• Methodology alignment with docs/methodology/REGISTRY.md looks consistent for the three‑DiD decomposition and IF‑based SEs; no mismatches found in code.
• P1: The new R‑comparison tests silently skip because required DGP CSV fixtures are missing, so the claimed R parity isn’t actually enforced.
• P3: Performance regression thresholds were loosened for CallawaySantAnna, which can mask unrelated regressions.

Methodology
No issues found.

Code Quality
No issues found.

Performance

• P3 | Impact: Performance regression guardrails are weakened for CallawaySantAnna, which can hide regressions unrelated to this PR. | Fix: Either keep prior thresholds, justify with measured CI variance, or add a TripleDifference‑specific perf test and leave CSA thresholds intact. Location: tests/test_se_accuracy.py:L404.

Maintainability
No issues found.

Tech Debt
No issues found.

Security
No issues found.

Documentation/Tests

• P1 | Impact: R‑comparison tests intended to validate parity with triplediff::ddd() are skipped because the DGP CSV fixtures are absent; this removes a key methodology verification step. | Fix: Add benchmarks/data/synthetic/ddd_r_dgp{1..4}.csv fixtures, or generate the DGP data deterministically in the test when files are missing. Location: tests/test_methodology_triple_diff.py:L125. Missing files: benchmarks/data/synthetic/ddd_r_dgp1.csv, benchmarks/data/synthetic/ddd_r_dgp2.csv, benchmarks/data/synthetic/ddd_r_dgp3.csv, benchmarks/data/synthetic/ddd_r_dgp4.csv.

Tests not run (review only).

Suggested next steps

1. Add or generate the missing R DGP CSV fixtures so R parity tests execute.
2. Decide whether to keep the relaxed perf thresholds or replace them with a TripleDifference‑specific benchmark.