A metaheuristic causal discovery method in directed acyclic graphs space

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Article number110749
Journal / PublicationKnowledge-Based Systems
Volume276
Online published30 Jun 2023
Publication statusPublished - 27 Sept 2023

Abstract

Causal discovery plays a vital role in the human understanding of the world. Searching a directed acyclic graph (DAG) from observed data is one of the most widely used methods. However, in most existing approaches, the global search has poor scalability, and the local search is often insufficient to discover a reliable causal graph. In this paper, we propose a generic metaheuristic method to discover the causal relationship in the DAG itself instead in of any equivalent but indirect substitutes. We first propose several novel heuristic factors to expand the search space and maintain acyclicity. Second, using these factors, we propose a metaheuristic algorithm to further search for the optimal solution closer to real causality in the DAG space. Theoretical studies show the correctness of our proposed method. Extensive experiments are conducted to verify its generalization ability, scalability, and effectiveness on real-world and simulated structures for both discrete and continuous models by comparing it with other state-of-the-art causal solvers. We also compare the performance of our method with that of a state-of-the-art approach on well-known medical data. © 2023 Elsevier B.V.

Research Area(s)

  • Causal discovery, Directed acyclic graph, Local search, Metaheuristics

Citation Format(s)

A metaheuristic causal discovery method in directed acyclic graphs space. / Liu, Xiaohan; Gao, Xiaoguang; Wang, Zidong et al.
In: Knowledge-Based Systems, Vol. 276, 110749, 27.09.2023.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review