Error-aware Markov blanket learning for causal feature selection

Causal feature selection has attracted much attention in recent years, since it has better robustness than the traditional feature selection. Existing causal feature selection algorithms aim to identify a Markov blanket (MB) of the class variable. The MB of the class variable implies potential local causal relations around the class variable and has been proven to be the optimal feature subset for feature selection. Since almost all existing causal feature selection methods employ conditional independence (CI) tests to learn MBs, in practical settings, existing causal feature selection algorithms encounter the problem of CI test errors, which seriously deteriorates the performance of those existing methods. To solve this issue, in this paper, we propose an Error-Aware Markov Blanket learning (EAMB) algorithm with two novel subroutines to tackle the CI test error problem. Specifically, EAMB first identifies the MB of the class variable using one subroutine, and then utilizes the other subroutine to selectively recover the missed true MB features from the discarded features. The extensive experiments on 13 real-world datasets validate the effectiveness of EAMB against fourteen state-of-the-art causal feature selection algorithms and four well-established traditional feature selection methods. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

Causal feature selection has gained much attention in recent years due to its improved robustness compared to traditional feature selection methods. However, existing algorithms that rely on conditional independence tests often encounter errors in practice, leading to degraded performance. In this paper, we propose an Error-Aware Markov Blanket learning algorithm with novel subroutines to address this issue, achieving better performance compared to state-of-the-art causal feature selection algorithms and traditional feature selection methods.