Matching using Semiparametric Propensity Scores

Steven Lehrer () and Gregory Kordas

No 441, Econometric Society 2004 North American Summer Meetings from Econometric Society

Abstract: Propensity score matching is becoming increasingly common in clinical medicine, demographic and economic research for the evaluation of the magnitude of treatment effects. Existing studies generally use parametric estimators of binary response models such as the probit and logit to estimate the propensity score, which imposes strong distributional assumptions on the error term that are often violated with the underlying data. This paper considers matching using semiparametrically estimated propensity scores. Our approach allows for heterogeneity in response across observed covariates along the conditional willingness to participate in the treatment intervention distribution. Data from the NSW experiment, CPS and PSID are used to evaluate the performance of alternative matching estimators. Preliminary estimates indicate mean absolute bias error reductions between 6.2% and 706% of the experimental treatment impact with stratification matching using semiparametric propensity score estimates relative to matching algorithms that employ parametric propensity scores

Keywords: Propensity Score matching; program evaluation; Binary quantile regression and heterogeneity (search for similar items in EconPapers)
JEL-codes: C14 C35 J00 (search for similar items in EconPapers)
Date: 2004-08-11
New Economics Papers: this item is included in nep-dcm
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Citations: View citations in EconPapers (2)

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Journal Article: Matching using semiparametric propensity scores (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:ecm:nasm04:441

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