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

Alexis Diamond and Jasjeet S. Sekhon
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Alexis Diamond: International Finance Corporation
Jasjeet S. Sekhon: University of California Berkeley and Institute of Governmental Studies

The Review of Economics and Statistics, 2013, vol. 95, issue 3, 932-945

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.

Keywords: matching; propensity score; selection on observables; genetic optimization; causal inference (search for similar items in EconPapers)
JEL-codes: C13 C14 H31 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (197)

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