Learning Causal Chain Graph Structure via Alternate Learning and Double Pruning

Abstract

Causal chain graphs model the dependency structure between individuals when the assumption of individual independence in causal inference is violated. However, causal chain graphs are often unknown in practice and require learning from data. Existing learning algorithms have certain limitations. Specifically, learning local information requires multiple subset searches, building the skeleton requires additional conditional independence testing, and directing the edges requires obtaining local information from the skeleton again. To remedy these problems, we propose a novel algorithm for learning causal chain graph structure. The algorithm alternately learns the adjacencies and spouses of each variable as local information and doubly prunes them to obtain more accurate local information, which reduces subset searches, improves its accuracy, and facilitates subsequent learning. It then directly constructs the chain graphs skeleton using the learned adjacencies without conditional independence testing. Finally, it directs the edges of complexes using the learned adjacencies and spouses to learn chain graphs without reacquiring local information, further improving its efficiency. We conduct theoretical analysis to prove the correctness of our algorithm and compare it with the state-of-the-art algorithms on synthetic and real-world datasets. The experimental results demonstrate our algorithm is more reliable than its rivals.