Tensor network method recovers moral graphs of causal DAGs from discrete distributions

Researchers describe a technique for inferring the moral graph of a causal directed acyclic graph using only the probability distribution over a set of discrete variables. The method represents the distribution with fully connected tensor networks and applies nuclear-norm regularization to the bond correction matrices to guide recovery. The paper adds a tensor-based approach to the toolkit for causal structure learning.