学术报告
Identifying effects of multiple treatments in the presence of unmeasured confounding - 苗旺（北京大学）
“2021首师大青年统计论坛”系列报告
题目：Identifying effects of multiple treatments in the presence of unmeasured confounding
报告人：苗旺（北京大学）
时间：2021年5月27日周四晚上20:00-21:00
地点：线上腾讯会议（会议号：709 455 5647）
Abstract : Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap between these statistical approaches and causal inference, but these attempts have either been shown to be flawed or have relied on fully parametric assumptions. In this paper, we propose two strategies for identifying and estimating causal effects of multiple treatments in the presence of unmeasured confounding. The auxiliary variables approach leverages auxiliary variables that are not causally associated with the outcome; in the case of a univariate confounder, our method only requires one auxiliary variable, unlike existing instrumental variable methods that would require as many instruments as there are treatments. An alternative null treatments approach relies on the assumption that at least half of the confounded treatments have no causal effect on the outcome, but does not require a priori knowledge of which treatments are null. Our identification strategies do not impose parametric assumptions on the outcome model and do not rest on estimation of the confounder. This work extends and generalizes existing work on unmeasured confounding with a single treatment, and provides a nonparametric extension of models commonly used in bioinformatics.
报告人简介：苗旺现为北京大学概率统计系助理教授， 2008-2017年在北京大学77779193永利官网读本科和博士，2017-2018年在哈佛大学生物统计系做博士后研究，2018年入职北京大学。苗旺的研究兴趣包括因果推断，缺失数据分析，及其在生物统计，流行病学，经济学和人工智能研究中的应用。
个人网页https://www.math.pku.edu.cn/teachers/mwfy/
联系人：周洁、胡涛
举办单位：77779193永利官网统计系 、北京应用统计学会、交叉科学研究院
