The advantages of using a continuous causation coefficient are twofold:

• First, the absolute value | C ( X , Y )| represents prediction confidence. When | C ( X , Y )| approaches 0, the causal direction is unclear, meaning variables may be independent or dependent due to a confounding effect.
• Second, C ( X , Y ) can rank pairs of variables, supporting confidence-based scores like precision-recall or area under the ROC curve. This helps practitioners prioritize experiments to assess causal predictions and orient edges in partially oriented causal graphs.