New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators

Matias Busso, John DiNardo and Justin McCrary
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Justin McCrary: University of California Berkeley and NBER

The Review of Economics and Statistics, 2014, vol. 96, issue 5, 885-897

Abstract: Fr�lich (2004) compares the finite sample properties of reweighting and matching estimators of average treatment effects and concludes that reweighting performs far worse than even the simplest matching estimator. We argue that this conclusion is unjustified. Neither approach dominates the other uniformly across data-generating processes (DGPs). Expanding on Fr�lich's analysis, this paper analyzes empirical as well as hypothetical DGPs and also examines the effect of misspecification. We conclude that reweighting is competitive with the most effective matching estimators when overlap is good, but that matching may be more effective when overlap is sufficiently poor.

Keywords: reweighting; propensity score (search for similar items in EconPapers)
Date: 2014
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