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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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    3. Miana Plesca & Jeffrey Smith, 2008. "Evaluating multi-treatment programs: theory and evidence from the U.S. Job Training Partnership Act experiment," Studies in Empirical Economics, in: Christian Dustmann & Bernd Fitzenberger & Stephen Machin (ed.), The Economics of Education and Training, pages 293-330, Springer.
    4. María Jesús Mancebón & Domingo P. Ximénez-de-Embún & Mauro Mediavilla & José María Gómez-Sancho, 2019. "Does the educational management model matter? New evidence from a quasiexperimental approach," Empirical Economics, Springer, vol. 56(1), pages 107-135, January.
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    7. 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.
    8. 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.
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    More about this item

    Keywords

    Propensity Score matching; program evaluation; Binary quantile regression and heterogeneity;
    All these keywords.

    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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