Selective Inference for Effect Modification: An Empirical Investigation

Abstract:We demonstrate a selective inferential approach for discovering and making confident conclusions about treatment effect heterogeneity. Our method consists of two stages. First, we use Robinson’s transformation to eliminate confounding in the observational study. Next we select a simple model for effect modification using lasso-regularized regression and then use recently developed tools in selective inference to make valid statistical inference for the discovered effect modifiers. We analyze the Mindset Study data-set provided by the workshop organizers and compare our approach with other benchmark methods.