A Differential Measure of the Strength of Causation

Abstract

We present the problem of measuring the strength of a causal interaction, starting from the linear perspective and generalizing to a nonlinear measure of causal influence. The proposed measure of causal strength is interpretable and we demonstrate that it may be estimated efficiently using Gaussian process regression. We validate our results on several examples and connect our results to the existing causal inference literature.