报告问题：The synthetic instrument: From sparse association to sparse causation
报告人：王林勃，多伦多大学统计科学系、盘算机与数学科学系助理教授
主持人：姜竹青副教授
报告时间：2023年4月26号（周三）10：00-11：00
报告所在：教三-105
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 L0 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.
专家简介：
王林勃于2011年北京大学获得统计学本科学位，并于2016年获得华盛顿大学统计学博士学位，之后在哈佛大学的公共卫生学院担当博士后，现在是多伦多大学统计科学系和盘算机数学科学系的助理教授 。他照旧Vector研究所的西席隶属成员，加拿大国家统计科学和盘算智能协会安简陋省STAGE项目的导师，并且是华盛顿大学统计系和多伦多大学盘算机科学系的联合助理教授 。他的研究兴趣集中在因果推断及其与统计学和机械学习的交互 。他的研究效果很是突出，现在已在统计学公认的四大期刊上揭晓十几篇主要文章，包括5篇JRSSB，2篇JASA, 6篇Biometrika，另外还在许多很是优异的统计学期刊发论文，好比Biometrics，Statistica Sinica，Statistics in Medicine 。
