Venue: ICLR 2022 Workshop DGM4HSD

Reviews:

final_score: rating: 8: Top 50% of accepted papers, clear accept
review_content: **Goals:** The authors introduce a novel method, called DAG-GFlowNet, to approximate the posterior distribution over Bayesian networks based on a dataset of observations.

**Description:** The description of the method is clear and can be replicated. The paper is clearly written and has examples.

**Evaluation:** Authors evaluate their work, and the claims given throughout this work are clearly articulated and supported by experiments on both simulated and real data. I also appreciate the authors touching on the limitations of their method in the conclusion section.

**Significance**: The paper is indeed interesting, and brings a novel contribution to the domain of Bayesian structure learning by improving over the standard framework of GFlowNets. The paper is appropriate for the workshop.

**Clarity:** The paper is written clearly and can be read easily. Some terminology could have been explained a bit better for readers like me who are not experts in this area. For instance, MCMC is mentioned in the abstract and the introduction, but authors never provided what it stands for. I guess most people would know what it is, but I believe it would be better if you simply provided what it stands for in your first mention of it.

The paper explains why the current terminology and techniques are insufficient (MCMC).

**Minor Corrections/Suggestions:**
- After "i.e." and "e.g.", always put a comma.
review_title: Review

final_score: rating: 7: Good paper, accept
review_content: In this paper, the authors propose a method for using Generative Flow Networks to approximate posterior distributions over the structure of Bayesian networks. 

The improvements over GFlowNet to make the support of the distribution coincide with DAGs seems important. The results look promising the method appears competitive with existing methods. That being said, though the results (Figure 2), are competitive with existing methods, the proposed method does not seem to stand out in any of the proposed metrics. It would be helpful to include some use cases where the proposed method would be preferred over MCMC or variational inference.
review_title: good paper

Decision: Accept (Oral)