Utilizing Expert Knowledge and Contextual Information for Sample-Limited Causal Graph Construction

Xuwu Wang, Xueyao Jiang, Sihang Jiang, Zhixu Li, Yanghua Xiao

Abstract: This paper focuses on causal discovery, which aims at inferring the underlying causal relationships from observational samples. Existing methods of causal discovery rely on a large number of samples. So when the number of samples is limited, they often fail to produce correct causal graphs. To address this problem, we propose a novel framework: Firstly, given an expert-specified causal subgraph, we leverage contextual and statistical information of the variables to expand the subgraph with positive-unlabeled learning. Secondly, to ensure the faithfulness of the causal graph, with the expanded subgraph as the constraint, we resort to a structural equation model to discover the entire causal graph. Experimental results show that our method achieves significant improvement over the baselines, especially when only limited samples are given.