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

Matias Busso (), John Enrico DiNardo and Justin McCrary ()
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Justin McCrary: University of California, Berkeley

No 3998, IZA Discussion Papers from Institute for the Study of Labor (IZA)

Abstract: Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting performs far worse than even the simplest matching estimator. We resolve this puzzle. We show that the findings from the finite sample analyses are not inconsistent with asymptotic analysis, but are very specific to particular choices regarding the implementation of reweighting, and fail to generalize to settings likely to be encountered in actual empirical practice. In the DGPs studied here, reweighting typically outperforms propensity score matching.

Keywords: treatment effects; propensity score; semiparametric efficiency (search for similar items in EconPapers)
JEL-codes: C14 C21 C52 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2009-02
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