Progressive Skeleton Learning for Effective Local-to-Global Causal Structure Learning

Abstract

Causal structure learning (CSL) from observational data is a crucial objective in various machine learning applications. Recent advances in CSL have focused on local-to-global learning, which offers improved efficiency and accuracy. The local-to-global CSL algorithms first learn the local skeleton of each variable in a dataset, then construct the global skeleton by combining these local skeletons, and finally orient edges to infer causality. However, data quality issues such as noise and small samples often result in the presence of problematic asymmetric edges during global skeleton construction, hindering the creation of a high-quality global skeleton. To address this challenge, we propose a novel local-to-global CSL algorithm with a progressive enhancement strategy and make the following novel contributions: 1) To construct an accurate global skeleton, we design a novel strategy to iteratively correct asymmetric edges and progressively improve the accuracy of the global skeleton. 2) Based on the learned accurate global skeleton, we design an integrated global skeleton orientation strategy to infer the correct directions of edges for obtaining an accurate and reliable causal structure. Extensive experiments demonstrate that our method achieves better performance than the existing CSL methods.