报告人：刘中华
报告地点：腾讯会议ID:892 243 524
报告时间：2021年12月16日星期四10:00-11:00
报告摘要：
Mendelian randomization (MR) is a popular instrumental variable (IV) approach, in which genetic markers are used as IVs. In order to improve efficiency, multiple markers are routinely used in MR analyses, leading to concerns about bias due to possible violation of IV exclusion restriction of no direct effect of any IV on the outcome other than through the exposure in view. To address this concern, we introduce a new class of Multiply Robust MR (MR2) estimators that are guaranteed to remain consistent for the causal effect of interest provided that at least one genetic marker is a valid IV without necessarily knowing which IVs are invalid. We show that the proposed MR2 estimators are a special case of a more general class of estimators that remain consistent provided that a set of at least k† out of K candidate instrumental variables are valid, for k†≤K set by the analyst ex ante, without necessarily knowing which IVs are invalid. We provide formal semiparametric theory supporting our results, and characterize the semiparametric efficiency bound for the exposure causal effect which cannot be improved upon by any regular estimator with our favourable robustness property. We conduct extensive simulation studies and apply our methods to a large-scale analysis of UK Biobank data, demonstrating the superior empirical performance of MR2 compared to competing MR methods.
主讲人简介：
刘中华博士现任香港大学统计及精算学系助理教授，主要研究方向是统计遗传学，因果推断，大规模统计推断等领域。刘中华博士于2015年毕业于哈佛大学生物统计系，后加入美国纽约摩根斯坦利投资银行从事量化金融工作。
