Review for NeurIPS paper: Counterfactual Prediction for Bundle Treatment

Additional Feedback: As mentioned above, I think this method is very nice, but should be framed differently. In particular, the issue being addressed is not confounding _bias_; it is sample inefficiency when estimating the regression model f_{\theta_p}. This distinction is important in the causal inference literature, because a bias does not disappear with sample size. However, in this context, under the unconfoundedness assumption, if the model f_{\theta_p} is sufficiently flexible, it will converge to the same true counterfactual model in the large sample limit regardless of how the data are weighted (this is consistent with the experiments in the paper). In other words, the population risks E_{cf} and E_f w are minimized at the same function.