Block Domain Knowledge-Driven Learning of Chain Graphs Structure

Abstract

As the interdependence between arbitrary objects in the real world grows, it becomes gradually important to use chain graphs containing directed and undirected edges to learn the structure among objects. However, independence among some variables corresponds to multiple structures and the direction of edges among variables cannot be uniquely determined. This limitation restricts existing chain graphs structure learning algorithms to only learning their Markov equivalence class. To alleviate this limitation, we de ne the block domain knowledge and propose a block domain knowledge-driven learning chain graphs structure algorithm (KDLCG). The KDLCG algorithm learns the adjacencies and spouses of all variables, which are utilized to directly construct the skeleton and orient the edges of the complexes, thereby learning the Markov equivalence class of the chain graphs. Subsequently, the KDLCG algorithm then updates some edges with Meek rules, guided by block domain knowledge. Finally, the KDLCG algorithm directs some edges by estimating causal effects between two variables, driven by block domain knowledge. Meanwhile, we conduct theoretical analysis to prove the correctness of our algorithm and compare it with the LCD algorithm and MBLWF algorithm on synthetic and real-world datasets. The experimental results validate the effectiveness of our algorithm.