TROP, a triply robust panel estimator relaxing parallel trends, ships official Python and Stata packages
alex_peys · x · 2026-10-13
GSB Causal Inference Lab announced official Python (pip install trop) and Stata packages for the Triply Robust Panel (TROP) estimator, built by Zhaonan Qu, Xuelin Yang, Justin Waddy and Damian Clarke.
TROP addresses doubts about the parallel trends assumption in panel causal inference by combining unit weights, time weights, and a regression adjustment based on a low-rank factor model: its bias is bounded by the product of unit imbalance, time imbalance, and misspecification, making it asymptotically unbiased if any one removes the bias. It performed well in 20 of 21 simulation designs calibrated to widely used datasets like CPS and Penn World Table.
Related event: Stanford releases triply robust panel estimator TROP for Python and Stata(2 posts)→
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