Reinforcement Causal Structure Learning on Order Graph

TL;DR

This paper introduces RCL-OG, a reinforcement learning-based method that models order graphs to efficiently approximate the posterior distribution of causal DAGs, improving causal structure learning accuracy.

Contribution

It proposes a novel reinforcement learning approach using order graphs and deep Q-learning to better approximate the posterior distribution of causal orderings, reducing computational complexity.

Findings

RCL-OG accurately approximates the posterior distribution of causal orderings.

It outperforms existing causal discovery algorithms on synthetic and benchmark datasets.

The method demonstrates improved causal structure learning accuracy.

Abstract

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.