Abstract: 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 variables 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.

Bio：Linbo Wang is an assistant professor in the Department of Statistical Sciences and the Department of Computer and Mathematical Sciences, University of Toronto. He is also a faculty affiliate at the Vector Institute, a CANSSI Ontario STAGE program mentor, and an Affiliate Assistant Professor in the Department of Statistics, University of Washington, and Department of Computer Science, University of Toronto. Prior to these roles, he was a postdoc at Harvard T.H. Chan School of Public Health. He obtained his Ph.D. from the University of Washington. His research interest is centered around causality and its interaction with statistics and machine learning.