时间：2019年6月20日（周四） 上午10：30-11：30
地点：理科大楼A楼1514
题目：Learning heterogeneity in causal inference using sufficient dimension reduction
报告人：Luo Wei 浙江大学数据科学研究中心研究员
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.
报告人简介：
Luo We，美国宾夕法尼亚州立大学博士，2014-2018任职于Baruch College，现任浙江大学数据科学研究中心研究员。Luo Wei老师主要研究领域有高维数据分析、充分降维、半参数方法、因果推断等，已在统计学国际顶级期刊Annals of Statistics, Biometrika上发表多篇文章。