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Matching using Semiparametric Propensity Scores

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  • Steven Lehrer
  • Gregory Kordas

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

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Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 441.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:nasm04:441

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Keywords: Propensity Score matching; program evaluation; Binary quantile regression and heterogeneity;

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References

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Cited by:
  1. Miana Plesca & Jeffrey Smith, 2007. "Evaluating multi-treatment programs: theory and evidence from the U.S. Job Training Partnership Act experiment," Empirical Economics, Springer, vol. 32(2), pages 491-528, May.
  2. Bernd Fitzenberger & Michael Lechner & Jeffrey Smith, 2013. "Estimation of treatment effects: recent developments and applications," Empirical Economics, Springer, vol. 44(1), pages 1-11, February.
  3. Apps, Patricia & Mendolia, Silvia & Walker, Ian, 2013. "The impact of pre-school on adolescents’ outcomes: Evidence from a recent English cohort," Economics of Education Review, Elsevier, vol. 37(C), pages 183-199.
  4. Galdo, Jose C. & Smith, Jeffrey A. & Black, Dan A., 2007. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," IZA Discussion Papers 3095, Institute for the Study of Labor (IZA).

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