Causal Discovery

In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs (DAGs) and various generalizations which allow for some variables …

We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. We define "latent factor causal models" (LFCMs) as a restriction on causal DAG models with latent variables, which …