Learning Bayesian network structure using Markov blanket decomposition

Causal structure learning algorithms construct Bayesian networks from observational data. Using non-interventional data, existing constraint-based algorithms may return I-equivalent partially directed acyclic graphs. However, these algorithms do not fully exploit the graphical properties of Bayesian networks, and require many redundant tests that reduce both speed and accuracy. In this paper, we introduce ideas to exploit such properties to increase the speed and accuracy of causal structure learning for multivariate normal data. In numerical experiments on five benchmarking networks our proposed algorithm was faster and more accurate than recently-developed algorithms.

► We introduce two properties that enhance causal structure learning performance.
► We prove that the proposed algorithm works under several assumptions.
► The proposed algorithm outperforms recent algorithms in both speed and accuracy.