Parallel ensemble methods for causal direction inference
This work addresses the challenge of inferring causal directions from observational data, which is fundamental in data science, but it appears to be incremental as it builds on existing ensemble methods.
The paper tackles the problem of unstable causal direction inference by proposing new algorithms based on parallel ensemble frameworks, demonstrating improved accuracy and computational efficiency in experiments on both artificial and real-world datasets.
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.