Local Causal Structure Learning with Efficient Parents Discovery
TL;DR
A novel local causal structure learning algorithm with Efficient Parents Discovery, named EPD, which starts from discovering the MB of the target variable and then learns the PC sets of undetermined variables, and continues iteratively until the parents and children of the target variable are identified.
Abstract
Local causal structure learning aims to identify direct causes (parents) and effects (children) of a target variable. Recent advances in this field rely on learning the MB (Markov Blanket) of unidentified variables. However, most existing methods require extensive search to discover spouses during MB learning, and they often need to learn the MB within the PC (parents and children) set of a target variable to identify parents, which becomes computationally expensive when the parent set is large and complex. To address this issue, we propose a novel local causal structure learning algorithm with Efficient Parents Discovery, named EPD. Specifically, EPD introduces an MB discovery subroutine, MBDis, which first identifies some parents of the target variable using the V-structure, and then performs feature selection to exclude candidate spouses that are weakly related to the target variable, thereby reducing the size of the candidate spouse set and accelerating spouse discovery. Additionally, EPD incorporates an IdePC subroutine, which learns the PC sets of the unresolved variables to identify additional parents, reducing the search space of parents. With the proposed MBDis and IdePC subroutines, EPD adopts an MB-by-PC learning strategy; it starts from discovering the MB of the target variable and then learns the PC sets of undetermined variables. This process continues iteratively until the parents and children of the target variable are identified. Using 7 Bayesian networks and 1 real-world data, the experiments have verified the effectiveness of EPD, in comparison with 10 state-of-the-art methods.