Genetic matching for estimating causal effects: A general multivariate matching method for acheiving balance in observational studies

Genetic matching is a new method for performing multivariate matching which uses an evolutionary search algorithm to determine the weight each covariate is given. The method utilizes an evolutionary algorithm developed by Mebane and Sekhon (1998; Sekhon and Mebane 1998) that maximizes the balance of observed potential confounders across matched treated and control units. The method is nonparametric and does not depend on knowing or estimating the propensity score, but the method is greatly improved when a known or estimated propensity score is incorporated. Genetic matching reliably reduces both the bias and the mean square error of the estimated causal effect even when the property of equal percent bias reduction (EPBR) does not hold. When this property does not hold, matching methods—such as Mahalanobis distance and propensity score matching—often perform poorly. Even if the EPBR property does hold and the propensity score is correctly specified, in finite samples, estimates based on genetic matching have lower mean square error than those based on the usual matching methods. We present a reanalysis of the LaLonde (1986) job training dataset which demonstrates the benefits of genetic matching and which helps to resolve a longstanding debate between Dehejia and Wahba (1997; 1999; 2002; Dehejia 2005) and Smith and Todd (2001, 2005a,b) over the ability of matching to overcome LaLonde’s critique of nonexperimental estimators. Monte Carlos are also presented to demonstrate the properties of our method. [Author Abstract]
Diamond, A., & Jasjeet, S.S. (2006). Genetic matching for estimating causal effects: A general multivariate matching method for achieving balance in observational studies. UC Berkeley: Institute of Governmental Studies Working Paper Version 1.2. Available at