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

Alexis Diamond¹; Jasjeet S. Sekhon²

¹ International Finance Corporation · ² University of California, Berkeley

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.

DOI

10.1162/rest_a_00318

Volume

95 (3)

Pages

932-945

Language

en

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