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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

Suggested Citation

  • Steven Lehrer & Gregory Kordas, 2004. "Matching using Semiparametric Propensity Scores," Econometric Society 2004 North American Summer Meetings 441, Econometric Society.
  • Handle: RePEc:ecm:nasm04:441
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    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. repec:eee:quaeco:v:67:y:2018:i:c:p:45-50 is not listed on IDEAS
    5. Jose C. Galdo & Jeffrey Smith & Dan Black, 2008. "Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data," Annals of Economics and Statistics, GENES, issue 91-92, pages 189-216.
    6. Christos Makridis, 2015. "The Elasticity of Air Quality: Evidence from Millions of Households Across the United States," Discussion Papers 15-020, Stanford Institute for Economic Policy Research.

    More about this item

    Keywords

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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J00 - Labor and Demographic Economics - - General - - - General

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