Structure Learning for Bayesian Networks over Labeled DAGs
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:133-144, 2018.
Graphical models based on labeled directed acyclic graphs (LDAGs) allow for representing context-specific independence relations in addition to regular conditional independencies. Modeling such constraints has been demonstrated to be important for expressiveness, interpretation and predictive ability. In this paper, we build theoretical results that make constraint-based and exact score-based structure discovery possible for this interesting model class. In detail, we present the first constraint-based learning method for LDAGs. The orientation rules use context-specific independencies for principled orientation of additional (causal) edges. We also present the first exact score-based learning method for LDAGs, that employs a branch and bound for the especially computational demanding task of local score calculation, after which exact DAG search can be used. Simulations verify the good performance of our methods in different data analysis tasks.