The finite sample performance of inference methods for propensity score matching and weighting estimators

Hugo Bodory, Martin Huber, Lorenzo Camponovo and Michael Lechner

No 466, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland

Abstract: This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.

Keywords: inference; variance estimation; treatment effects; matching; inverse probability weighting (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2016-01-26
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (29)

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Related works:
Journal Article: The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators (2020)
Working Paper: The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators (2016)
Working Paper: The finite sample performance of inference methods for propensity score matching and weighting estimators (2016)
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