Speaker: Qingyuan Zhao, University of Cambridge Title: Using sparsity to overcome unmeasured confounding: Two examples Abstract: Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems motivated by genetics applications. The first problem is about removing “batch effects” or latent confounders in multiple hypothesis testing. I will present a general framework called Confounder Adjusted Testing…