主题：Estimation and Inference of Heterogeneous Treatment Effects

　　主讲：王思鉴，副教授，罗格斯大学(美国)

　　时间：2019年6月25日 09:30--11:00

　　地点：经济与管理学院B249

　　摘要：Many scientific and business challenges, ranging from personalized medicine to customized marketing recommendations, require an understanding of treatment effect heterogeneity. In this talk, based on the causal inference framework, we develop a flexible method for estimating heterogeneous treatment effects. The method can handle binary, continuous, time-to-event, and possibly contaminated outcomes in the same fashion. Furthermore, based on the potential outcome framework and confidence distribution (CD) framework, we propose a confidence measure to quantify the estimation uncertainty in personalized decisions. This measure, with value in [0,1], provides a frequency-based assessment about the decision. It is also shown to match well with the classical assessments of sensitivity and specificity, but without the need to know the true optimal treatment regime. Utility of the development is demonstrated in an adaptive sampling in sequential experiments.

　　讲座人简介：王思鉴，清华大学本科，美国密歇根大学博士，前威斯康辛大学麦迪逊分校统计系和生物统计及医药信息系副教授(已取得终身教授)，现美国罗格斯大学统计系副教授，博士生导师。王思鉴博士在顶尖学术刊物发表20余篇统计论文，曾获国际生物统计协会(ENAR)2007年的Van Ryzin Award，美国统计协会(ASA)2007年的最佳学生论文和泛统计协会的J. P. Hsu Memorial Award。他先后受邀在宾夕法尼亚大学，哥伦比亚大学等美国顶尖大学以及各类国际会议进行报告70余次。