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

Matí­as Busso; John DiNardo; Justin McCrary

doi:10.1162/rest_a_00431

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

Matías Busso¹; John DiNardo²; Justin McCrary^3,4

¹ Inter-American Development Bank · ² University of Michigan–Ann Arbor · ³ National Bureau of Economic Research · ⁴ University of California, Berkeley

The Review of Economics and Statistics 2014

Frölich (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 Frölich'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.

DOI: 10.1162/rest_a_00431
Volume: 96 (5)
Pages: 885-897
Language: en
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