  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
H\"aggstr\"om for her exploration of causal inference with high-dimensional
confounding, while making a call for further research in this same vein.
