The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators

Hugo Bodory (), Lorenzo Camponovo, Martin Huber and Michael Lechner
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Hugo Bodory: University of St. Gallen

No 9706, IZA Discussion Papers from IZA Network @ LISER

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: matching; treatment effects; variance estimation; inference; inverse probability weighting (search for similar items in EconPapers)
JEL-codes: C21 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2016-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Published - published in: Journal of Business & Economic Statistics, 2020, 38 (1), 183-200

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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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