Optimizing Variance-Bias Trade-Off in the TWANG Package for Estimation of Propensity Scores

Layla Parast, Daniel F. McCaffrey, Lane F. Burgette, Fernando Hoces de la Guardia, Daniela Golinelli, Jeremy N. V. Miles and Beth Ann Griffin

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: While propensity score weighting has been shown to reduce bias in treatment effect estimation when selection bias is present, it has also been shown that such weighting can perform poorly if the estimated propensity score weights are highly variable.

Keywords: Causal inference; Propensity score; Machine learning (search for similar items in EconPapers)
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