1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/analysis.py / out/report.md (Data and exclusions)",8 "issue": "The report says the SHA-256 matches the README, but the script only prints the hash and never compares it with the README value. I recomputed it and it does match (2cdda6cc...). The report also says 'the gaps between rows match business days'. The file actually has 10 two-day and 6 five-day calendar gaps. These look like holidays, but they are not listed in data_issues or broken down anywhere.",9 "suggested_fix": "Have the script assert the README hash. Describe the holiday gaps as checked and benign, for example in data_issues with handling 'none needed'."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/results.json:data_issues",15 "issue": "data_issues lists only the truncation and treatment problem. The negative audit results (no NaNs, no duplicates, dates monotone, post_cut consistent with its documented rule) appear only in the report and in state files.",16 "suggested_fix": "Optionally add a short audit-summary entry so results.json is self-contained."17 }18 ],19 "summary": "I re-ran analysis.py in a scratch copy and it reproduces results.json, table1.csv and exclusions.csv exactly. The data contain 2498 rows from 2010-01-04 to 2019-12-31, and every row has post_cut = 0. So the estimand cannot be computed, and premise_invalid / infeasible with null effect, CI, model_metric and n_analysed is the correct outcome. No proxy was substituted and post_cut was not redefined. baseline_metric (-0.04285 bp, the mean of 2497 changes) is correct. The claims match the numbers, and the limitations section is an honest failure discussion. Only minor documentation points remain."20}21