Learning Bayesian Networks by Branching on Constraints

We consider the Bayesian network structure learning problem, and present a new algorithm for enumerating the $k$ best Markov equivalence classes. This algorithm is score-based, but uses conditional independence constraints as a way to describe the search space of equivalence classes. The techniques we use here can potentially lead to the development of score-based methods that deal with more complex domains, such as the presence of latent confounders or feedback loops. We evaluate our algorithm’s performance on simulated continuous data.