Add survey real-data validation against R using federal survey datasets

[igerber]

Summary

• Validate diff-diff's survey variance against R's survey package using three real federal survey datasets
• Suite A (API): 8 tests — TSL variance with strata, FPC, subpopulations, covariates, and Fay's BRR replicates from R's canonical apistrat dataset
• Suite B (NHANES): 4 tests + 1 skipped — TSL with real CDC strata + PSU + nest=TRUE, using ACA young adult coverage provision (2007-08 vs 2015-16)
• Suite C (RECS): 3 tests — JK1 replicate weight variance with 60 real EIA replicate columns
• All metrics (ATT, SE, df, CI) match R to machine precision (< 1e-10 differences)
• Add real-data section to survey tutorial (Section 10) demonstrating NHANES ACA DiD with actual CDC data
• Document results in docs/benchmarks.rst with reproduction instructions
• Exclude large data files from AI review diff to avoid Codex input limit

Methodology references

• N/A — no estimator or math changes. This PR adds validation tests, benchmark scripts, and documentation only.

Validation

• Tests added: tests/test_survey_real_data.py (15 tests + 1 skip)
• R benchmark scripts: benchmarks/R/benchmark_realdata_{api,nhanes,recs}.R
• Tutorial updated: docs/tutorials/16_survey_did.ipynb (Section 10: real NHANES data)
• Results validated against published ACA literature (Antwi et al. 2013, Sommers 2012)

Security / privacy

• Confirm no secrets/PII in this PR: Yes
• NHANES data is public-use (no geographic identifiers, no individual names)
• RECS data is public-use microdata from EIA

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Overall Assessment
✅ Looks good

No unmitigated P0/P1 findings. The PR does not change estimator implementations, weighting math, variance formulas, identification checks, or defaults; the main issues are P2/P3 gaps in the new validation harness and in how its guarantees are documented. Static review only: pytest was not available in this environment, so I could not execute the new suite here.

Executive Summary

• No estimator-level methodology deviation was introduced. The changed code is test/docs/benchmark scaffolding, and the new subpopulation tests follow the Methodology Registry’s domain-estimation pattern.
• P2: the skipped NHANES repeated-cross-section CallawaySantAnna path is wired to inconsistent time scales between the R benchmark generator and the Python test, so it is not reproducible as written.
• P2: the docs/changelog claim full machine-precision ATT/SE/df/CI parity across 15 tests, but the pytest module only enforces a subset of those metrics and uses tolerances that are far looser than that claim.
• P3: realdata is added as a marker, but default pytest still runs it because only slow is excluded.
• P3: the AI review workflow now omits full diffs for benchmark JSON/CSV fixtures and tutorial notebooks, creating a blind spot for future review automation.

Methodology

• P2. Impact: the new NHANES RC-DiD cross-validation path is not aligned to the estimator’s time/first_treat contract. The R script remaps to period_cs = period + 1 and first_treat_cs = 2 before calling did::att_gt, but the Python test calls CallawaySantAnna(panel=False) on the original period=0/1 data while keeping first_treat=2. In CallawaySantAnna, first_treat is interpreted on the same scale as time, and overall aggregation only uses post-treatment cells, so this wiring leaves cohort g=2 with no post-treatment cells on the Python side. It is currently masked only because the committed NHANES golden file does not contain b5_cs_rc. Locations: benchmarks/R/benchmark_realdata_nhanes.R:172, benchmarks/R/benchmark_realdata_nhanes.R:203, benchmarks/data/real/nhanes_aca_subset.csv:1, tests/test_survey_real_data.py:396, diff_diff/staggered.py:1539, diff_diff/staggered_aggregation.py:56. Concrete fix: export/use the remapped period_cs and first_treat_cs values in the JSON fixture and Python test, or remap to 1/2 in the Python test before calling fit(). If B5 is intentionally unsupported, remove it from the claimed validation suite rather than leaving a mismatched dormant path.
• No other methodology findings. I did not find any estimator-code change that conflicts with docs/methodology/REGISTRY.md.

Code Quality

• No findings.

Performance

• P3. Impact: the new file is tagged realdata, but default pytest selection still runs it because addopts excludes only slow. If that is intentional, the marker is informational only; if not, the suite routing is inconsistent and baseline CI cost rises unnecessarily. Locations: tests/test_survey_real_data.py:54, pyproject.toml:99. Concrete fix: either change default addopts to exclude realdata and run these in a dedicated job, or mark them slow if they are meant to be opt-in.

Maintainability

• P3. Impact: the AI PR review workflow now strips all benchmark JSON/CSV diffs and tutorial notebooks from the prompt. Those are the substantive artifacts in this PR, so future automated review will only see filenames, not the actual fixture/notebook changes. Location: ai_pr_review.yml:150. Concrete fix: narrow the exclusion to raw/generated assets only, or append compact summaries for excluded files (row counts, column names, hashes, notebook cell/output counts) so review automation still has visibility.

Tech Debt

• No findings.

Security

• No findings.

Documentation/Tests

• P2. Impact: the public validation claim is stronger than what CI enforces. The new test module reuses synthetic-suite tolerances (ATT_ATOL=0.01, SE_ATOL=0.05, replicate SE_ATOL=0.1) and several scenarios do not assert all exported metrics. Examples: B2-B4 only check ATT/SE; A5/A7 only check ATT/SE; A4 only checks ATT plus finite/positive SE; C3 does not compare to the exported R deff_diagnostics at all. Meanwhile the docs and changelog say all 15 tests and all ATT/SE/df/CI metrics match R to machine precision. That overstates the current guardrail; some NHANES SE checks could pass even with materially wrong SEs. Locations: tests/test_survey_real_data.py:40, tests/test_survey_real_data.py:199, tests/test_survey_real_data.py:345, tests/test_survey_real_data.py:464, tests/test_survey_real_data.py:542, benchmarks/R/benchmark_realdata_recs.R:160, docs/benchmarks.rst:599, docs/survey-roadmap.md:294, CHANGELOG.md:11. Concrete fix: tighten tolerances to the observed scale of the committed golden files, add the missing df/CI assertions wherever the golden fixtures already export them, compare C3 to the exported DEFF reference or relabel it as a smoke test, and soften the docs/changelog until the tests actually enforce the stronger claim.

[igerber]

/ai-review

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: eca4ac34fa233863fccfb1dd2d1ab763e05c99e4

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Overall Assessment

⚠️ Needs changes

Highest unmitigated severity: P1.

Executive Summary

• Prior issues were partially addressed: the Python suite removed NHANES B5 and tightened numerical tolerances from percent-level slack to 1e-8.
• Severity P1 [Newly identified]: the new A5 API subpopulation validation explicitly waives df/CI parity against R, even though the Methodology Registry still says subpopulation survey df_survey should match survey::degf() after subset(). That leaves a real inference discrepancy undocumented and untested.
• Severity P2: the docs/changelog still claim machine-precision ATT/SE/df/CI parity across all 15 tests, but several scenarios still assert only subsets of those outputs.
• Severity P2: the NHANES R benchmark script still contains the optional B5 path while the committed golden JSON/tests no longer do, so the published “rerun the benchmark, then run the tests” workflow remains environment-dependent.
• Severity P3: the new realdata marker is not excluded by default, and the AI review workflow now hides notebook diffs without surfacing a summary.
• Static review only: I could not run the suite here because the sandbox Python environment is missing dependencies (numpy import failed).

Methodology

• Severity P1 [Newly identified]. Impact: the new A5 test/comment accepts a survey-df mismatch for a subpopulation/domain-estimation case (Python=397 vs R=199) and therefore skips CI/df validation exactly where inference differs, while the Registry still states that subpopulation df_survey matches R after subset(). Because survey df feeds downstream inference, this leaves subpopulation CI/p-values inconsistent with the claimed reference behavior and undocumented. References: tests/test_survey_real_data.py:219, tests/test_survey_real_data.py:237, docs/methodology/REGISTRY.md:2361, benchmarks/R/benchmark_realdata_api.R:219, docs/benchmarks.rst:657, diff_diff/survey.py:590. Concrete fix: either make subpopulation survey df/CI follow the benchmarked domain design so A5 can assert df/CI against R, or add a **Note:** / **Deviation from R:** entry to the Registry and downgrade A5/public claims to ATT/SE-only parity.

Code Quality

• No findings.

Performance

• Severity P3. Impact: realdata is registered but default pytest still runs it because addopts excludes only slow; the new marker is informational only. References: pyproject.toml:99, tests/test_survey_real_data.py:55. Concrete fix: add not realdata to default addopts, or also mark the suite slow.

Maintainability

• Severity P3. Impact: the AI PR review workflow now excludes docs/tutorials/*.ipynb in addition to the large benchmark assets and removed the explicit excluded-file notice, so future automated review will miss substantive tutorial/notebook changes in this feature. References: .github/workflows/ai_pr_review.yml:150. Concrete fix: restore an excluded-file summary (hashes, notebook cell counts, fixture key lists) or stop dropping notebook source diffs entirely.

Tech Debt

• No findings.

Security

• No findings.

Documentation/Tests

• Severity P2. Impact: public validation claims are still stronger than the assertions. The changelog and benchmarks doc say ATT/SE/df/CI match to machine precision across 15 tests, but A4 checks ATT only, A5 checks ATT/SE only, A7 omits CI despite fixture CI values, C1/C2 omit CI despite fixture CI values, and C3 does not compare the exported DEFF reference. docs/benchmarks.rst also still says the old “1–5% tolerances” were not approached even though the code now uses 1e-8. References: CHANGELOG.md:11, docs/benchmarks.rst:599, docs/benchmarks.rst:729, tests/test_survey_real_data.py:200, tests/test_survey_real_data.py:265, tests/test_survey_real_data.py:460, tests/test_survey_real_data.py:538. Concrete fix: add the missing CI/DEFF assertions where the fixture already contains reference values, or narrow the docs/changelog to the exact metrics each scenario actually enforces.
• Severity P2. Impact: the NHANES reproduction path is still nondeterministic. The Python tests and committed nhanes_realdata_golden.json removed B5, but the R generator still emits B5 whenever did is installed, and the documented benchmark requirements explicitly install did. Following the published reproduction steps can therefore regenerate a different artifact than the committed fixture while the roadmap says “4 tests passing, 1 skipped.” References: benchmarks/R/benchmark_realdata_nhanes.R:163, benchmarks/R/requirements.R:7, tests/test_survey_real_data.py:426, docs/survey-roadmap.md:296, docs/benchmarks.rst:749. Concrete fix: remove or hard-disable B5 in the main NHANES benchmark script, or move it into a separate optional script not referenced by the main reproduction flow.

Path to Approval

1. Resolve the A5 subpopulation inference contract: either align SurveyDesign.subpopulation() survey df/CI with the benchmarked R domain behavior and then assert A5 df/CI in the real-data test, or add a Registry **Note:** / **Deviation from R:** entry and remove all “Exact” / machine-precision df/CI claims for A5.
2. Make NHANES benchmark reproduction deterministic by deleting or hard-skipping B5 in benchmarks/R/benchmark_realdata_nhanes.R, or by moving it to a separate optional benchmark not referenced by the committed fixture/docs.
3. Bring the public validation claims into line with what CI actually checks, or add the missing assertions for A7/C1/C2/C3 (and clarify the narrower scope of A4/A5).

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: 26c16458b4d0f59198458cb98d0ab9bc5c5e6451

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Overall Assessment

✅ Looks good

Highest unmitigated severity: P2.

Executive Summary

• Prior P1 methodology issues appear resolved. The A5 subpopulation df difference is now explicitly documented as a Deviation from R in the registry, and the dormant NHANES B5 path was removed from both the R benchmark and Python suite.
• The main remaining issue is a documentation/test-scope mismatch for A4: the public benchmark docs still imply exact df parity there, but the test only enforces ATT parity and a finite positive SE.
• The AI PR review workflow still excludes notebook diffs entirely, so future automated reviews will miss substantive tutorial changes like this PR’s new NHANES section.
• The committed notebook outputs include local absolute filesystem paths in stderr, which is low-severity but unnecessary environment leakage.
• Static review only. I could not run pytest here because pytest is not installed in the environment.

Methodology

No unmitigated methodology findings. The previously raised A5 subpopulation df divergence is now documented as a Deviation from R in docs/methodology/REGISTRY.md:2367, and that matches the current subpopulation() implementation in diff_diff/survey.py:422. The non-deterministic NHANES B5 path was also removed from both the R benchmark and Python suite in benchmarks/R/benchmark_realdata_nhanes.R:162 and tests/test_survey_real_data.py:430.

Code Quality

No findings.

Performance

• Severity P3. Impact: the new realdata marker is informational only; default pytest still runs these tests because addopts filters only not slow, so the new suite becomes part of ordinary runs whenever the fixtures exist. References: pyproject.toml:99, pyproject.toml:100, tests/test_survey_real_data.py:56. Concrete fix: add not realdata to default addopts, or also mark this module slow if the intended behavior is opt-in execution.

Maintainability

• Severity P3. Impact: the AI PR review workflow still drops notebook diffs from the compiled prompt, so automated review will miss substantive tutorial changes like the new NHANES section added in this PR. References: .github/workflows/ai_pr_review.yml:150, .github/workflows/ai_pr_review.yml:154, docs/tutorials/16_survey_did.ipynb:1509. Concrete fix: restore an explicit excluded-file summary, or include an nbconvert/cell-source summary for excluded notebooks in the review prompt.

Tech Debt

No findings.

Security

• Severity P3. Impact: the committed notebook outputs expose local workstation and temp paths in stderr (for example /Users/igerber/... and /var/folders/...). This is not a credential leak, but it is unnecessary environment metadata in a published artifact. References: docs/tutorials/16_survey_did.ipynb:370, docs/tutorials/16_survey_did.ipynb:1284, docs/tutorials/16_survey_did.ipynb:1729. Concrete fix: clear notebook outputs before commit, or scrub path-bearing stderr from saved outputs.

Documentation/Tests

• Severity P2. Impact: the public validation docs still overstate what CI actually enforces for A4. The benchmark page says all comparisons match to machine precision and marks A4 df as exact, but the test only checks ATT parity plus finite/>0 SE, so a TWFE df regression would not be caught by CI. References: docs/benchmarks.rst:599, docs/benchmarks.rst:657, docs/survey-roadmap.md:294, tests/test_survey_real_data.py:200. Concrete fix: either add an A4 assertion for result.survey_metadata.df_survey == r["df"] (and any other intended comparable metric), or narrow the docs to ATT-only validation for A4 and qualify the opening “all comparisons” sentence accordingly.

[igerber]

/ai-review

[github-actions]

🔁 AI review rerun (requested by @igerber)

Head SHA: ceee94c1043c57d3df7c9f2d67cdfa337161308d

---

Overall Assessment

Looks good.

Highest unmitigated severity: P2.

Executive Summary

• No unmitigated methodology defects in the estimator code path. The notable A5 subpopulation df divergence is now explicitly documented in the methodology registry, so it is correctly treated as a documented deviation rather than a blocker.
• The main remaining issue is still a docs/test-scope mismatch for A4: public docs still describe broader R parity than the committed TWFE test actually enforces.
• The AI review workflow now explicitly excludes notebook diffs, so future automated reviews will miss substantive tutorial changes like this PR’s new NHANES section.
• The earlier notebook-output path-leakage concern appears resolved; I did not find the prior absolute local/temp paths in the committed notebook text.
• Static review only; pytest is not installed in this environment, so I did not execute the new suite.

Methodology

• No unmitigated methodology findings.
• Severity P3 (informational). Impact: the A5 subpopulation df difference from R is now properly documented as a Deviation from R, and the test correspondingly avoids treating it as a failure; under the review rubric, this is no longer a defect. Concrete fix: none. Refs: docs/methodology/REGISTRY.md:L2361-L2374, tests/test_survey_real_data.py:L219-L238

Code Quality

• No findings.

Performance

• Severity P3 (informational). Impact: if realdata was intended to make the new suite opt-in, the current pytest defaults do not do that; default runs still include these tests whenever fixtures are present because only slow is excluded. Concrete fix: if opt-in behavior is desired, add not realdata to default addopts, or also mark the module slow. Refs: pyproject.toml:L99-L104, tests/test_survey_real_data.py:L55-L56

Maintainability

• Severity P3. Impact: the AI PR review workflow now hard-excludes docs/tutorials/*.ipynb from the compiled diff, so future automated reviews will miss substantive tutorial changes entirely; this PR’s new NHANES section is exactly the kind of change that becomes invisible. Concrete fix: include a notebook cell-source summary in the prompt (for example via nbconvert/jupytext), or restore an explicit excluded-file summary so reviewers know content was omitted. Refs: .github/workflows/ai_pr_review.yml:L150-L156, docs/tutorials/16_survey_did.ipynb:L1509-L1529

Tech Debt

• No findings.

Security

• No findings.

Documentation/Tests

• Severity P2. Impact: the public validation docs still overstate the scope of A4/TWFE parity. The benchmark page says all comparisons match to machine precision and still labels A4 df as exact, while the test only asserts ATT equality plus a finite/positive SE; the roadmap summary likewise says ATT/SE/df/CI match across the API variants before listing A4/A5 exceptions. That leaves room for a TWFE df/CI regression to slip through CI while docs continue to promise exact parity. Concrete fix: either add explicit A4 assertions for every comparable quantity the docs claim, or narrow the docs/roadmap language to ATT-only for A4 and “all directly comparable metrics” instead of blanket machine-precision parity. Refs: docs/benchmarks.rst:L599-L602, docs/benchmarks.rst:L657-L661, docs/benchmarks.rst:L734-L747, docs/survey-roadmap.md:L294-L303, tests/test_survey_real_data.py:L200-L217