Review for NeurIPS paper: Counterfactual Prediction for Bundle Treatment

The authors propose a method for doing weighted sample adjustment for learning counterfactual regression models when treatments are high-dimensional. The reviewers, after some discussion, converged on the view that the paper is a nice contribution to the estimation theory for causal effects. One area where the paper could benefit is a discussion of the connections of the author's results to results on semi-parametric efficiency theory and influence functions (see e.g. It is likely there is a close relationship between the role weights play in improving efficiency and efficient influence functions for the problem (even under randomization).