统计与数据科学系系列学术报告之三百九四
时 间：2023年05月09日（周二）10:00-11:00
主持人：复旦大学 管理学院 统计与数据科学系 朱仲义教授
地 点：李达三楼105室
报告人：Linbo Wang Assistant Professor Department of Computer & Mathematical Sciences, University of Toronto Scarborough
题 目：The synthetic instrument: From sparse association to sparse causation
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 $\ell_0$-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.
个人简介：王老师目前为多伦多大学统计科学系和计算机与数学科学系的Assistant Professor。在担任这些职务之前，他在北京大学获得本科学位，华盛顿大学获得博士学位，并在哈佛大学公共卫生学院进行博士后研究。王老师的研究兴趣主要集中在因果推断以及其在机器学习上的应用上，已在JRSSB, Biometrika, JASA, Epidemiology等国际权威期刊发表论文近二十篇。
统计与数据科学系
2023-4-21
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