From: NeurIPS.cc/2022/Workshop/SyntheticData4ML/Paper49/Area_Chair_8qeT
recommendation: Accept

From: NeurIPS.cc/2022/Workshop/SyntheticData4ML/Paper49/Reviewer_RGWr
title: Causal model-based simulation for policy optimization
review: This paper proposes a simsim framework where a temporal causal model is learned from simulated data and subsequently used for optimizing treatment dosing policy. 

Pros:

1. Using temporal causal model for simulation is well-motivated. This work leverages causal BayesNet and DECI to serve as the causal model. The learned causal graph helps ensure that the downstream simulation is grounded. 

2. Using simulation to optimize policy is also a proven approach in related fields such as reinforcement learning.

Cons:

1. A more in-depth comparison of the three approaches would be appreciated. What are the respective strengths and weakness, the assumptions and prior knowledge required?
rating: 6: Marginally above acceptance threshold
confidence: 4: The reviewer is confident but not absolutely certain that the evaluation is correct

From: NeurIPS.cc/2022/Workshop/SyntheticData4ML/Paper49/Reviewer_7tQb
title: Interesting paper, perhaps lacking some model details
review: I think the paper is quite interesting. Model based policy optimisation seems like a great fit to be combined with causal discovery. There were a few things that were lacking for me:
- As this is a workshop on synthetic data, and the focus on the paper is more causal modelling and policy optimisation. It would make more sense to submit elsewhere.
- How are the models learned/optimised? There seem to be little details on the actual learning process


rating: 6: Marginally above acceptance threshold
confidence: 3: The reviewer is fairly confident that the evaluation is correct