Discovery and inference of possibly bi-directional causal relationships with invalid instrumental variables
報告人簡介
李偉，中國人民大學統計學院副教授，中國人民大學吳玉章青年學者，入選國家高層次青年人才計劃。研究方向是因果推斷、缺失數據及其在生物醫學、社會經濟學等領域中的應用，已在JRSSB, Biometrika, JoE等著名國際統計學期刊發表多篇文章。主持國家自然科學基金面上和青年項目、北京市自然科學基金面上項目等多項課題，擔任中國現場統計研究會因果推斷分會副秘書長。
內容簡介
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.
