报告简介  Abstract

Treatment effects estimation is one of the crucial mainstays in medical and epidemiological studies. Ignorance of the existence of confounders may result in biased estimates. 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 unobservable confounders. The proposed estimator is shown to be consistent and asymptotically normally distributed. A statistic is also developed for testing the existence of treatment effects. Simulation studies show that our proposed estimator is relatively stable for various unobservable confounding settings. Finally, we apply our proposed methodologies to a low birthweight data set which yields different conclusions with and without the consideration of possible unobservable confounders.

嘉宾简介  About the Speaker

高巍，东北师范大学数学与统计学院教授，主要从事的有序数据统计分析、面板数据分析、序约束下统计推断，先后访问过日本数理统计研究所、伦敦政治经济学院等。主持多项国家自然科学基金。

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