走向现代数学学术报告 - 王林勃教授（No. 699）

题目: Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

报告人：王林勃 教授（多伦多大学）

时间：2024年4月30日 15:30

地点：东海岸校区-D实209

摘要：Instrumental variable methods provide useful tools for inferring causal effects in the presence of unmeasured confounding. To apply these methods with large-scale data sets, a major challenge is to find valid instruments from a possibly large candidate set. In practice, most of the candidate instruments are often not relevant for studying a particular exposure of interest. Moreover, not all relevant candidate instruments are valid as they may directly influence the outcome of interest. In this article, we propose a data-driven method for causal inference with many candidate instruments that addresses these two challenges simultaneously. A key component of our proposal is a novel resampling method, which constructs pseudo variables to remove irrelevant candidate instruments having spurious correlations with the exposure. Synthetic data analyses show that the proposed method performs favourably compared to existing methods. We apply our method to a Mendelian randomization study estimating the effect of obesity on health-related quality of life.

报告人简介：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任博士后，目前是多伦多大学统计科学系和计算机数学科学系的教授。他还是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教授。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。他的研究成果非常突出，根据不完全统计，目前他已在统计学公认的四大期刊上发表十几篇重要文章，包括5篇JRSSB，2篇JASA, 7篇Biometrika，另外还在很多非常优秀的统计学期刊发论文，比如Biometrics，Statistica Sinica，Statistics in Medicine。