The advantage of using a continuous causation coefficient is twofold.

• On one hand, the absolute value $| C ( X , Y )|$ is interpreted as the confidence of the prediction. When $| C ( X , Y )|$ goes to 0, the causal direction is unclear; variables could be considered as either independent, or dependent because of a confounding effect.
• On the other hand, $C ( X , Y )$ is used to rank pairs of variables, supporting the definition of confidence based scores such as the precision-recall score or the area under the ROC curve score. From a practitioner’s viewpoint, $C ( X , Y )$ can be used to prioritize experiments in order to assess causal predictions. Additionally, $C ( X , Y )$ allows practitioners to orient edges in partially oriented causal graphs.