  This paper introduces a causation coefficient which is defined in terms of
probabilistic causal models. This coefficient is suggested as the natural
causal analogue of the Pearson correlation coefficient and permits comparing
causation and correlation to each other in a simple, yet rigorous manner.
Together, these coefficients provide a natural way to classify the possible
correlation/causation relationships that can occur in practice and examples of
each relationship are provided. In addition, the typical relationship between
correlation and causation is analyzed to provide insight into why correlation
and causation are often conflated. Finally, example calculations of the
causation coefficient are shown on a real data set.
