报告题目：New Estimation Procedures of Conditional Average Treatment Effects
报告人：童行伟 教授 北京师范大学
报告时间：2021年6月11日 10：00-11：00
报告地点：腾讯会议 会议 ID：780 619 962 会议密码：0611
校内联系人：王培洁 wangpeijie@jlu.edu.cn
报告摘要：Conditional average treatment effect (CATE) is designed to capture the heterogeneity of treatment effect across subpopulations. In this paper, we propose a new nonparametric estimation strategy for CATE based on the propensity score and projection theory. The proposed approach has two advantages over the existing ones. First, it does not need to estimate the two nonparametric regression functions of the outcome on many covariates for treated and control groups, and obtain their predicted values via extrapolation. Second, the proposed method includes propensity score as a new covariate in nonparametric regression model, thus it can effectively overcome the hazardous impact due to extreme weights (propensity score close to 0 or 1) in weighting estimators. Meanwhile, the proposed procedure does not rely on outcome model specication. We establish the consistency of the proposed estimator, and further show that it asymptotically follows an normal distribution and the associated variance can be estimated. Simulation studies indicate that the proposed procedures outperform competing ones. We further illustrate the proposed procedures by an empirical analysis of a real-world dataset.
报告人简介：童行伟，北京师范大学统计学院教授，博士生导师。主要从事生物统计，金融统计等方向的研究。中国现场统计研究会常务理事，概率统计学会常务理事,《应用概率统计》杂志编委；主持一项科技部重点研发计划子课题，主持国家自然基金面上项目3项，教育部重大科研项目1项，目前发表学术论文近50篇。
