Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery

Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement learning method for causal discovery based on tree search that builds directed acyclic graphs incrementally. We also formalize and prove the correctness of an efficient algorithm for excluding edges that would introduce cycles, which enables deeper discrete search and sampling in DAG space. The proposed method can be applied broadly to causal Bayesian networks with …

biology cs.ai cs.lg dag decision discovery economics fields graphs making prove reinforcement reinforcement learning search space tree work