GENETIC MATCHING FOR ESTIMATING CAUSAL EFFECTS: A GENERAL MULTIVARIATE MATCHING METHOD FOR ACHIEVING BALANCE IN OBSERVATIONAL STUDIES

• 成果类型: Article
• 署名作者: Diamond, Alexis; Sekhon, Jasjeet S.
• 署名单位: University of California System; University of California Berkeley
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/REST_a_00318
• 发表日期: 2013-07
• 页码: 932-945
• 关键词: propensity score estimation; training-programs; BIAS; regression; optimization; performance; outcomes; package; designs; IMPACT

摘要

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.