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Controlling for overlap in matching

Author

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  • Paweł Strawiński

    (Faculty of Economic Sciences, University of Warsaw)

Abstract

The overlap problem is crucial in propensity score matching. Recently, Crump et al. (2009) showed that trimming provides a simple and robust solution to the overlap problem. In this study, we use a simulation approach to show that trimming is inferior to the caliper mechanism. We show that in most cases, both techniques provide unbiased estimates, but trimming is less efficient.

Suggested Citation

  • Paweł Strawiński, 2013. "Controlling for overlap in matching," Working Papers 2013-10, Faculty of Economic Sciences, University of Warsaw.
    • Handle: RePEc:war:wpaper:2013-10
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    File URL: http://www.wne.uw.edu.pl/inf/wyd/WP/WNE_WP95.pdf
    File Function: First version, 2013
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    References listed on IDEAS

    as
    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    5. Lee, Myoung-jae, 2005. "Micro-Econometrics for Policy, Program and Treatment Effects," OUP Catalogue, Oxford University Press, number 9780199267699.
    6. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute of Labor Economics (IZA).
    7. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    8. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    9. Zhong Zhao, 2004. "Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 91-107, February.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Wenlian Gao & Feifei Zhu & Kai Chen, 2023. "The role of bank lenders in firm leverage adjustments," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 63-97, February.

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    More about this item

    Keywords

    average treatment effect; overlap; propensity score; caliper; trimming.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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