报告人简介

李伟，中国人民大学统计学院副教授，中国人民大学吴玉章青年学者，入选国家高层次青年人才计划。研究方向是因果推断、缺失数据及其在生物医学、社会经济学等领域中的应用，已在JRSSB, Biometrika, JoE等著名国际统计学期刊发表多篇文章。主持国家自然科学基金面上和青年项目、北京市自然科学基金面上项目等多项课题，担任中国现场统计研究会因果推断分会副秘书长。

内容简介

Learning causal relationships between pairs of complex traits from observational studies is of great interest across various scientific domains. However, most existing methods assume the absence of unmeasured confounding and restrict causal relationships between two traits to be uni-directional, which may be violated in real-world systems. In this paper, we address the challenge of causal discovery and effect inference for two traits while accounting for unmeasured confounding and potential feedback loops. By leveraging possibly invalid instrumental variables, we provide identification conditions for causal parameters in a model that allows for bi-directional relationships, and we also establish identifiability of the causal direction under the introduced conditions. Then we propose a data-driven procedure to detect the causal direction and provide inference results about causal effects along the identified direction. We show that our method consistently recovers the true direction and produces valid confidence intervals for the causal effect. We conduct extensive simulation studies to show that our proposal outperforms existing methods. We finally apply our method to analyze real data sets from UK Biobank.