MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming

TL;DR

This paper introduces MEC-IP, an integer programming-based algorithm that efficiently discovers Markov Equivalence Classes of Bayesian Networks, significantly reducing computation time and improving causal discovery accuracy.

Contribution

The paper proposes a novel IP approach with clique-focusing and EMSG strategies, advancing the efficiency and accuracy of MEC discovery in Bayesian Networks.

Findings

Reduced computational time compared to existing methods

Improved causal discovery accuracy across datasets

Effective in complex data structures

Abstract

This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maximal Spanning Graphs (EMSG) to streamline the search for MEC, thus overcoming the computational limitations inherent in other existing algorithms. Our numerical results show that not only a remarkable reduction in computational time is achieved by our algorithm but also an improvement in causal discovery accuracy is seen across diverse datasets. These findings underscore this new algorithm's potential as a powerful tool for researchers and practitioners in causal discovery and BNSL, offering a significant leap forward toward the efficient and accurate analysis of complex data structures.