报告题目：Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
报告人：孔德含 助理教授 加拿大多伦多大学统计系
报告时间：2023年7月4日 9:00
报告地点：数学楼第一报告厅
校内联系方式：王培洁 wangpeijie@jlu.edu.cn
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.
报告人简介：Dr.Dehan Kong is an Associate Professor in statistics at the University of Toronto. His main research area focuses on neuroimaging data analysis, statistical machine learning, causal inference, and statistical genetics and genomics. He is currently an Associate Editor for the Journal of the American Statistical Association.
