Review for NeurIPS paper: Counterfactual Predictions under Runtime Confounding

AC's comments before receiving the ethics review (see below): The authors had a lively and detailed discussion and settled on the following points: - The framing of the paper seems reasonable: it seems plausible that one may have certain variables in past data that may not exist, or be allowed during prediction time. There's a similar line of work on this in algorithmic fairness which is very plausible given GDPR, companies not wanting to be sued for violating laws, or even HIPAA/security issues. They do buy the authors argument that their approach will work better because imputation is a strictly harder problem. However the realism of the setting makes them think this is still a useful problem to address, even if it is much simpler than other causal settings. I urge the authors to modify the paper according to the suggestions of reviewers.