报告时间：2022年6月29日

               上午10:00-11:00

报告地点：腾讯会议

              （会议ID：562 228 183）

报告嘉宾：孔德含

报告主题：Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

报告摘要

Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

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.

个人简介

孔德含，多伦多大学统计学助理教授，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。现任美国统计学会会刊副主编。