Reviews: Computationally and statistically efficient learning of causal Bayes nets using path queries

This paper gives algorithms for recovering the structure of causal Bayesian networks. The main focus is on using path queries, that is asking whether a direct path exists between two nodes. Unlike with descendant queries, with path queries one could only hope to recover the transitive structure (an equivalence class of graphs). The main contribution here is to show that at least this can be done in polynomial time, while each query relies on interventions that require only a logarithmic number of samples. The author do this for discrete and sub-Gaussian random variables, show how the result can be patched up to recover the actual graph, and suggest specializations (rooted trees) and extensions (imperfect interventions).