Discussion of "Data-driven confounder selection via Markov and Bayesian networks" by Jenny Häggström

Abstract

In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal inference in the presence of colliders, point out an under-appreciated advantage of double robustness, discuss the relative difficulty of independence testing versus regression, and finally commend Häggström for her exploration of causal inference with high-dimensional confounding, while making a call for further research in this same vein.