A causation coefficient and taxonomy of correlation/causation relationships

TL;DR

This paper proposes a causation coefficient based on probabilistic causal models, enabling a rigorous comparison between correlation and causation, and introduces a taxonomy of their possible relationships with practical examples.

Contribution

It introduces a new causation coefficient analogous to Pearson's correlation, facilitating classification and analysis of correlation/causation relationships.

Findings

Causation coefficient defined and explained

Taxonomy of correlation/causation relationships provided

Real data examples illustrating calculations

Abstract

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.