Parallel ensemble methods for causal direction inference

• Published in: Journal of parallel and distributed computing Vol. 150; pp. 96 - 103
• Main Authors: Zhang, Yulai, Wang, Jiachen, Cen, Gang, Lo, Kueiming
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2021
• Subjects: Causal direction inference; Parallel ensemble; Unstable learner; Causal direction inference; Parallel ensemble; Unstable learner
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1016/j.jpdc.2020.12.012

Inferring the causal direction between two variables from their observation data is one of the most fundamental and challenging topics in data science. A causal direction inference algorithm maps the observation data into a binary value which represents either x causes y or y causes x. The nature of these algorithms makes the results unstable with the change of data points. Therefore the accuracy of the causal direction inference can be improved significantly by using parallel ensemble frameworks. In this paper, new causal direction inference algorithms based on several ways of parallel ensemble are proposed. Theoretical analyses on accuracy rates are given. Experiments are done on both of the artificial data sets and the real world data sets. The accuracy performances of the methods and their computational efficiencies in parallel computing environment are demonstrated. •Parallel ensemble algorithms for causal direction inference are proposed.•Theoretical analyses of the accuracy rates are given.•Performances on both artificial and real world data are demonstrated.