【主题】Learning heterogeneity in causal inference using sufficient dimension reduction

【报告人】骆威, 副教授

浙江大学

【时间】 2019年10月29日 10:00-11:00

【地点】 上海财经大学统计与管理学院大楼1208会议室

【摘要】Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that can be used in three ways: to conduct variable selection for the regression causal effect, to improve the estimation accuracy of the regression causal effect, and to detect the heterogeneity of the regression causal effect. Compared with the literature, our approaches adopt a weaker sufficient dimension reduction assumption, and do not rely on parametric modeling of the regression causal effect or any modeling of the individual outcome regressions. These advantages are illustrated by both simulation studies and a real data example.

【嘉宾简介】骆威，2018年秋季加入浙江大学数据科学研究中心，研究方向是充分降维方法及其在因果推断等领域的应用。

【主持人】王绍立