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

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  • Matias Busso

    (Inter-American Development Bank)

  • John DiNardo

    (University of Michigan and NBER)

  • Justin McCrary

    (University of California Berkeley and NBER)

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.

Suggested Citation

  • Matias Busso & John DiNardo & Justin McCrary, 2014. "New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 885-897, December.
    • Handle: RePEc:tpr:restat:v:96:y:2014:i:5:p:885-897
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    Keywords

    reweighting; propensity score;

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