  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.
