Functional causal discovery

Functional causal discovery (also called function-based causal discovery) is all about leveraging the information about the functional forms and properties of distributions governing the relationships between variables in order to uniquely identify causal directions in a dataset. In this section, we’ll introduce the logic behind function-based methods, using the Additive Noise Model (ANM) (Hoyer et al., 2008) and LiNGAM (Shimizu et al., 2006) as examples. We’ll implement ANM and LiNGAM and discuss the differences between the two. By the end of this section, you will have a good understanding of the general principles of function-based causal discovery and you’ll be able to apply the ANM and LiNGAM models to your own problems using Python and gCastle.

The blessings of asymmetry

Tyger Tyger, burning bright,

In the forests of the night;

What immortal hand or eye,

Could frame thy fearful symmetry?

William Blake –...