报告人：苗旺
报告地点：腾讯会议ID: 651206042
报告时间：2024年11月22日星期五19:00-20:00
报告摘要：
Unmeasured confounding poses a significant challenge in identifying and estimating causal effects across various research domains. Existing methods to address confounding often rely on either parametric models or auxiliary variables, which strongly rest on domain knowledge and could be fairly restrictive in practice. In this paper, we propose a novel strategy for identifying causal effects in the presence of confounding under an additive structural equation with light-tailed confounding. This strategy uncovers the causal effect by exploring the relationship between the exposure and outcome at the extreme, which can bypass the need for parametric assumptions and auxiliary variables. The resulting identification is versatile, accommodating a multi-dimensional exposure, and applicable in scenarios involving unmeasured confounders, selection bias, or measurement errors. Building on this identification approach, we develop an Extreme-based Causal Effect Learning (EXCEL) method and further establish its consistency and non-asymptotic error bound. The asymptotic normality of the proposed estimator is established under the linear model. The EXCEL method is applied to causal inference problems with invalid instruments to construct a valid confidence set for the causal effect. Simulations and a real data analysis are used to illustrate the potential application of our method in causal inference.
主讲人简介：
苗旺现为北京大学概率统计系和统计科学中心副教授， 2008-2017年在北京大学数学科学学院读本科和博士，2017-2018年在哈佛大学生物统计系做博士后研究，2018年入职北京大学光华管理学院，2020年调入数学科学学院。苗旺的研究兴趣包括因果推断，缺失数据，半参数统计，及其应用，与合作者提出混杂分析的代理推断理论，发展非随机缺失数据的识别性和双稳健估计理论，以及数据融合的半参数理论。个人网页https://www.math.pku.edu.cn/teachers/mwfy
