As an emerging concept cognitive learning model, partial order formal structure analysis (POFSA) has been widely used in the field of knowledge processing. In this paper, we propose the method named three-way causal attribute partial order structure (3WCAPOS) to evolve the POFSA from set coverage to causal coverage in order to increase the interpretability and classification performance of the model. First, the concept of causal factor (CF) is proposed to evaluate the causal correlation between attributes and decision attributes in the formal decision context. Then, combining CF with attribute partial order structure, the concept of causal attribute partial order structure is defined and makes set coverage evolve into causal coverage. Finally, combined with the idea of three-way decision, 3WCAPOS is formed, which makes the purity of nodes in the structure clearer and the changes between levels more obviously. In addition, the experiments are carried out from the classification ability and the interpretability of the structure through the six datasets. Through these experiments, it is concluded the accuracy of 3WCAPOS is improved by 1% - 9% compared with classification and regression tree, and more interpretable and the processing of knowledge is more reasonable compared with attribute partial order structure.

翻译：作为一种新兴的概念认知学习模型，偏序形式结构分析（POFSA）已在知识处理领域得到广泛应用。本文提出了一种名为三支因果属性偏序结构（3WCAPOS）的方法，将POFSA从集合覆盖演化为因果覆盖，以提升模型的可解释性和分类性能。首先，提出因果因子（CF）概念，用于评估形式决策上下文中属性与决策属性之间的因果关联。随后，将CF与属性偏序结构相结合，定义因果属性偏序结构，使集合覆盖演化为因果覆盖。最后，结合三支决策思想形成3WCAPOS，使结构中节点的纯度更加清晰，层级间变化更加明显。此外，通过六个数据集从分类能力和结构可解释性两方面开展实验。实验结果表明，与分类回归树相比，3WCAPOS的准确率提高了1%至9%，且相较于属性偏序结构具有更强的可解释性，知识处理也更为合理。