Reinforcement Causal Structure Learning on Order Graph
This work addresses the problem of causal discovery from limited data for researchers and practitioners in fields like machine learning and statistics, offering an incremental improvement over existing methods by reducing computational intractability.
The paper tackles the challenge of learning directed acyclic graphs (DAGs) for causal structure discovery by proposing RCL-OG, which uses reinforcement learning on an order graph to approximate posterior distributions of DAG orderings, achieving better results than competitive algorithms on synthetic and benchmark datasets.
Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose {Reinforcement Causal Structure Learning on Order Graph} (RCL-OG) that uses order graph instead of MCMC to model different DAG topological orderings and to reduce the problem size. RCL-OG first defines reinforcement learning with a new reward mechanism to approximate the posterior distribution of orderings in an efficacy way, and uses deep Q-learning to update and transfer rewards between nodes. Next, it obtains the probability transition model of nodes on order graph, and computes the posterior probability of different orderings. In this way, we can sample on this model to obtain the ordering with high probability. Experiments on synthetic and benchmark datasets show that RCL-OG provides accurate posterior probability approximation and achieves better results than competitive causal discovery algorithms.