Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies
提出一种使用进化搜索算法确定协变量权重的多元匹配方法，能改善协变量平衡并可能降低偏差，适用于观察性研究的因果效应估计。
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.