Hosted by the GPH Department of Biostatistics
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 talk, Dr. Dehan Kong proposes a data-driven method for causal inference with many candidate instruments that addresses these two challenges simultaneously. A key component of the 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. He applies this method to a Mendelian randomization study estimating the effect of obesity on health-related quality of life.
About the Speaker:
Dr. Dehan Kong is an Associate Professor in statistics at the University of Toronto. His research interests focus on biomedical data science, with the goal of developing advanced statistical tools and methodologies to handle large, complex, multi-scale real-world biomedical data. He is currently an Associate Editor for the Journal of the American Statistical Association, Applications and Case Studies.