报告题目：The synthetic instrument: from sparse association to sparse causation
报告人： 王林勃 （多伦多大学）
报告时间： 5月16日 10:00-11:00
报告地点：维格堂319
报告摘要：In many observational studies, researchers are interested in studying the effects of multiple exposures on the same outcome. Unmeasured confounding is a key challenge in these studies as it may bias the causal effect estimate. To mitigate the confounding bias, we introduce a novel device, called the synthetic instrument, to leverage the information contained in multiple exposures for causal effect identification and estimation. We show that under linear structural equation models, the problem of causal effect estimation can be formulated as an l_0-penalization problem, and hence can be solved efficiently using off-the-shelf software. Simulations show that our approach outperforms state-of-art methods in both low-dimensional and high-dimensional settings. We further illustrate our method using a mouse obesity dataset.
报告人简介：王林勃是多伦多大学统计科学系和计算机数学科学系的助理教授，Vector研究所的教师附属成员，加拿大国家统计科学和计算智能协会安大略省STAGE项目的导师，并且是华盛顿大学统计系和多伦多大学计算机科学系的联合助理教授。在此之前，他曾在哈佛公共卫生学院担任博士后，并于华盛顿大学获得博士学位。他的研究兴趣集中在因果推断及其与统计学和机器学习的交互。
邀请人：马学俊