Used to infer causal direction by modeling P(y) as a convolution of two non Gaussian distributions, in a linear model X -> Y with non Gaussian noise. P(x) is arguably less Gaussian than P(y), and this 'less' can be measured numerically to give evidence of a direction of causality.