Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting.

翻译：因果发现旨在揭示变量间的因果结构。基于评分的方法主要依据预定义的评分函数搜索最佳有向无环图（DAG）。然而，由于搜索能力受限，大多数方法无法应用于大规模场景。受生成流网络中主动学习的启发，我们提出了一种从观测数据学习DAG的新方法——GFlowCausal。该方法将图搜索问题转化为生成问题，逐步添加有向边。GFlowCausal旨在通过学习最优策略，以与预定义奖励成比例的概率，通过顺序动作生成高奖励的DAG。我们提出了一个基于传递闭包的即插即用模块，以确保高效采样。理论分析表明，该模块能有效保证无环特性，并确保最终状态与完全连接图之间的一致性。我们在合成数据集和真实数据集上进行了大量实验，结果表明所提方法具有优越性，且在大规模场景中表现良好。