Matching using semiparametric propensity scores

Steven Lehrer () and Gregory Kordas

Empirical Economics, 2013, vol. 44, issue 1, 13-45

Abstract: This paper considers the application of semiparametric methods to estimate propensity scores or probabilities of program participation, which are central to certain program evaluation methods. To evaluate the practical benefits, we first conduct a Monte Carlo study. Second, we use data from the NSW experiment, CPS, and PSID. We compare treatment effect and evaluation bias estimates using propensity scores estimated from parametric logit, semiparametric single index, and semiparametric binary quantile regression models. Our results suggest that it is important to account for very general forms of heterogeneity in (semiparametric) estimation of the propensity score, particularly when the treatment effects vary in an unsystematic manner with the true propensity score. Copyright Springer-Verlag 2013

Keywords: Propensity score matching; Treatment effects; Semiparametric binary choice estimators; Heterogeneity; Binary quantile regression; C14; C81; C99; H53; I38 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (7)

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Working Paper: Matching using Semiparametric Propensity Scores (2004)
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DOI: 10.1007/s00181-012-0591-3

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