Towards Efficient Local Causal Structure Learning

TL;DR

This paper introduces ELCS, an efficient algorithm for local causal structure learning that improves accuracy and speed by integrating Markov Blanket discovery with N-structures to distinguish direct causes and effects of a target variable.

Contribution

The paper proposes a novel ELCS algorithm that combines N-structures with an efficient Markov Blanket discovery method for improved local causal structure learning.

Findings

ELCS outperforms existing algorithms in accuracy.

ELCS demonstrates higher efficiency in experiments.

Validated on eight Bayesian networks.

Abstract

Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To tackle this issue, we propose a novel Efficient Local Causal Structure learning algorithm, named ELCS. Specifically, we first propose the concept of N-structures, then design an efficient Markov Blanket (MB) discovery subroutine to integrate MB learning with N-structures to learn the MB of T and simultaneously distinguish direct causes from direct effects of T. With the proposed MB subroutine, ELCS starts from the target variable, sequentially finds MBs of variables connected to the target variable and simultaneously constructs local causal structures over MBs until the direct causes and direct effects of the target variable have been distinguished. Using eight Bayesian networks the extensive experiments have validated that ELCS achieves better accuracy and efficiency than the state-of-the-art algorithms.