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Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies

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  • Alexis Diamond

    (International Finance Corporation)

  • Jasjeet S. Sekhon

    (University of California Berkeley and Institute of Governmental Studies)

Abstract

This paper presents genetic matching, a method of multivariate matching that uses an evolutionary search algorithm to determine the weight each covariate is given. Both propensity score matching and matching based on Mahalanobis distance are limiting cases of this method. The algorithm makes transparent certain issues that all matching methods must confront. We present simulation studies that show that the algorithm improves covariate balance and that it may reduce bias if the selection on observables assumption holds. We then present a reanalysis of a number of data sets in the LaLonde (1986) controversy. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    • Handle: RePEc:tpr:restat:v:95:y:2013:i:3:p:932-945
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    Keywords

    matching; propensity score; selection on observables; genetic optimization; causal inference;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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