Structural learning of causal networks

Causal network models are popular statistical tools to represent dependencies or causal relationships among variables in complex systems. Structural learning of causal networks is crucial to discover the causal knowledge and to infer casual effects. In this paper, we discuss structural learning of two types of graphical models, undirected graphs and directed acyclic graphs. We first introduce the methods for learning undirected graphical models. Then we discuss structural learning of directed acyclic graphs. We focus on the issues on model space of causal networks, decomposition learning of structures from observational data, local structural learning approaches and the active learning for optimal designs of intervention.