Review for NeurIPS paper: Deep Structural Causal Models for Tractable Counterfactual Inference

POST REBUTTAL -- I have read the authors' responses and other reviewers' comments. Unfortunately, some of my primary concerns have not been addressed, which I will elaborate on below. This paper studies the implementation of Pearl's in a SCM, where each of its functions is represented as a neural network. The authors claim that the proposed approaches "are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals giving rise to a powerful new approach for answering causal questions in imaging applications and beyond." However, I believe the significance of its contributions to the causal inference literature is a bit overstated. In particular, the authors assume that detailed parameterization of the target SCM is *precisely known*.