Parallel ensemble methods for causal direction inference

TL;DR

This paper introduces parallel ensemble frameworks to improve the stability and accuracy of causal direction inference algorithms, supported by theoretical analysis and experiments on artificial and real-world datasets.

Contribution

It proposes new parallel ensemble algorithms for causal inference, enhancing accuracy and computational efficiency over existing methods.

Findings

Improved accuracy in causal direction inference with parallel ensembles.

Enhanced computational efficiency in parallel computing environments.

Theoretical analysis confirms the stability and effectiveness of the proposed methods.

Abstract

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.