Local Causal Discovery Towards High‐Dimensional Streaming Features

Causal discovery focuses on identifying a target's direct causes and effects (e.g., class label) feature of interest in a Bayesian network (BN). Existing causal discovery primarily includes global and local learning algorithms, which must access the whole feature space before the learning process starts. However, many real‐world applications continuously generate features in real‐time and demand stream processing of features for just‐in‐time decision‐making. In addition, the local and global learning algorithms either emphasise accuracy or computing efficiency over both. Therefore, to address these problems and handle dynamic high‐dimensional feature space, we proposed a novel local causal discovery algorithm based on streaming features that consider improving and balancing both computational efficiency and prediction accuracy denoted as L ocal C ausal D iscovery Towards High‐Dimensional S treaming F eatures . More specifically, to attain this objective, dynamically integrates ‐ and ‐structure to learn the Markov blanket (MB) and simultaneously distinguishes the direct causes (parents) from direct effects (children) and parents–children (PC) from spouses of the target feature. Thus accomplishes the balance between efficiency and accuracy of prediction. The proposed algorithm has been extensively evaluated on 10 benchmark BNs and 10 real‐world datasets. The results show that the proposed algorithm outperformed the existing state‐of‐the‐art baseline algorithms. The source code is available at: https://github.com/vickykhan89/LCDSF .