Reviews: Multi-domain Causal Structure Learning in Linear Systems

The authors leverage on data recorded in multiple domains with changing parameters of a linear Gaussian models to learn causal direction and relations beyond Markov equivalence class. The paper is clearly written and includes good synthetic data simulations. The result is theoretically interesting. The method seems to need a lot of samples and domains that may not be available in real cases. The authors present different options to improve the methods in this respect.