报告人：孔德含
报告地点：数学与统计学院415
报告时间：2023年7月5日上午10:00-11:00
邀请人：朱文圣
报告摘要：
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.
主讲人简介：
孔德含，多伦多大学统计学副教授，研究方向包括脑图像，函数型数据分析，因果推断，高维数据分析以及机器学习。研究成果发表在统计学国际顶级期刊JRSSB，JASA，Biometrika等，现任统计学期刊JASA副主编。
