Multi-label causal feature selection has attracted extensive attention in recent years. However, current methods primarily operate at the label level, treating each label variable as a monolithic entity and overlooking the fine-grained causal mechanisms unique to individual categories. To address this, we propose a Category-level Multi-label Causal Feature selection method named Ca-MCF. Ca-MCF utilizes label category flattening to decompose label variables into specific category nodes, enabling precise modeling of causal structures within the label space. Furthermore, we introduce an explanatory competition-based category-aware recovery mechanism that leverages the proposed Specific Category-Specific Mutual Information (SCSMI) and Distinct Category-Specific Mutual Information (DCSMI) to salvage causal features obscured by label correlations. The method also incorporates structural symmetry checks and cross-dimensional redundancy removal to ensure the robustness and compactness of the identified Markov Blankets. Extensive experiments across seven real-world datasets demonstrate that Ca-MCF significantly outperforms state-of-the-art benchmarks, achieving superior predictive accuracy with reduced feature dimensionality.

翻译：近年来，多标签因果特征选择已引起广泛关注。然而，现有方法主要在标签层面进行操作，将每个标签变量视为单一整体，忽略了各个类别特有的细粒度因果机制。为解决此问题，本文提出一种名为Ca-MCF的类别级多标签因果特征选择方法。Ca-MCF利用标签类别扁平化技术，将标签变量分解为具体的类别节点，从而实现对标签空间内因果结构的精确建模。此外，我们引入一种基于解释性竞争的类别感知恢复机制，利用所提出的特定类别互信息与差异类别互信息，以恢复被标签相关性掩盖的因果特征。该方法还结合了结构对称性检验与跨维度冗余消除策略，以确保所识别马尔可夫毯的鲁棒性与紧凑性。在七个真实数据集上的大量实验表明，Ca-MCF显著优于当前最先进的基准方法，能够在降低特征维度的同时获得更优的预测精度。