报告题目：Primal-dual Covariate Balance and Minimal Double Robustness via Entropy Balancing

报告人：Qingyuan Zhao, Ph.D candidate, Stanford University

报告时间：2015年9月21日      下午4:00-5:00

报告地点：管理科研楼1208教室

报告摘要：

Covariate balance is a conventional key diagnostic for methods used estimating causal effects from observational studies. We show covariate balance can also be directly used in the estimation of mean causal effects by studying a recently proposed entropy maximization method called Entropy Balancing (EB). In order to avoid the tautology of repeatedly checking covariate balance, EB exactly matches the covariate moments for the different experimental groups in its optimization problem. We find that the primal and dual problems of EB correspond, respectively, to the implicit linear model of outcome and propensity score logistic regression, with features being the balanced moments. Consequently, we prove EB enjoys some desirable statistical properties: it is doubly robust with respect to the linear models and reaches the asymptotic semiparametric variance bound when both models are correct. Our theoretical results and simulations suggest EB is a very appealing alternative to the conventional propensity score weighting estimators.