報告題目：Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
報告人：孔德含 助理教授 加拿大多倫多大學統計系
報告時間：2023年7月4日 9:00
報告地點：數學樓第一報告廳
校内聯系方式：王培潔 wangpeijie@jlu.edu.cn
報告摘要：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.
報告人簡介：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.
