Fix weight propagation and trimming logic#72
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alexanderquispe merged 6 commits intod2cml-ai:mainfrom Jan 12, 2026
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Report.pdf
I corrected five identified bugs. As a result, the Python package now produces outputs that exactly match those of the reference R and Stata implementations.
Bugs Fixed:
Bug 1. Weight Propagation Failure: User-specified weights were silently ignored in the esti-
mation pipeline.
Bug 2. Panel Data Misrouting: Unbalanced panel data was incorrectly routed to repeated
cross-section (RCS) estimators.
Bug 3. Universal Base Period Logic Error: Time index was compared against year values,
causing influence function shape mismatches.
Bug 4. MissingPropensityScoreTrimming: DRDID-defaulttrimming(trim_level=0.995)
was not applied to IPW/DR estimators.
Bug 5. Scalar Aggregation Crash: Single-group aggregations failed due to scalar vs. array
handling.