Review for NeurIPS paper: Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

Weaknesses: - Some of the points I'll make here are more conceptual and would just like to hear from the authors what their thoughts are. However, there is another school of thought related to the Nonparanormal distribution that says everything can be transformed into something that looks Gaussian. In practice, either in applications or real-world analyses the authors have undertaken, what has been their experience in the usage of Gaussian vs non-Gaussian methods for structure learning. Is the first method they recommend a non-Gaussian method or one that relies on Gaussianity assumptions? In that sense, it is closer to algorithms like PC/FCI that are in theory nonparametric.