The semantic analysis of modal verbs faces many challenges due to their inherent complexity， requiring the extraction of diverse feature sets for Word Sense Disambiguation （WSD）， including semantic， syntactic， pragmatic， and genre features. These features vary in the contribution to semantic disambiguation， and some are confusing or redundant. To eliminate the influence of confusing and redundant features on WSD， a word sense disambiguation approach based on Causal Partial Order Diagram （CPOD） was proposed. Learning from the concept of intervention in causal reasoning， eliminating confusing factors through do-calculus， and combining with the idea of constructing an Attribute Partial Order Diagram （APOD）， a CPOD was developed as a model for WSD of modal verbs. Results of WSD experiments on 15 English modal verbs showed that the proposed method achieved an average accuracy of 93.42%， eliminating confusing and redundant features. Furthermore， to quantify the specific contribution of each feature to WSD， each feature's contribution was calculated and ranked. It is found that semantic features contribute the most， followed by syntactic features， while genre features contribute relatively less. Specifically， in semantic features， features with low mutual information contribute much more to WSD than those with high mutual information， making them the most influential features.