报告题目：Estimating Treatment Effects in the Presence of Unobserved Confounders

报告人：高巍，东北师范大学教授、博士生导师。

照片：

邀请人：李本崇

报告时间：2020年8月12日下午15:00—16:30

报告平台：腾讯会议：910 960 972

报告人简介：东北师范大学教授、博士生导师，公司党委书记。主要从事的研究方向是约束条件下统计推断、有序离散数据统计分析、离散金融时间序列，计量经济学。先后获得留学归国基金、国家自然科学基金、教育部新世纪人才支持计划项目的资助，参与多项国家自然科学基金。先后访问日本数理统计研究所、美国密苏里大学（University of Missouri）、英国伦敦政治经济学院、香港大学、香港中文大学等。主要的学术贡献是将广泛应用于统计学、信息学、计算机科学上的GIS (Generalized Iterative Scaling) 方法扩展到更一般的情形，提出了UGIS (Unified Generalized Iterative Scaling) 方法。

报告摘要：Treatment effects estimation is one of the crucial mainstays in medical and epidemiological studies. Ignorance of the existence of confounders may result in biased estimators. The issue will become more serious and complicated if the treatment is endogenous (i.e., the presence of unobserved confounders). In this article, we propose a new treatment effects estimator for binary treatments in observational studies in the presence of unobserved confounders. The proposed estimator is consistent and asymptotically normally distributed. A statistic is also developed for testing the existence of treatment effects. Simulation studies show that the proposed estimator is stable for various unobserved confounding settings and the distribution of error terms. The proposed estimator is superior to the local average treatment effects estimator. Finally, we apply our proposed methodologies to a low birthweight data set which yields different conclusions with and without the consideration of possible unobserved confounders.