Inferring deterministic causal relations

About

We consider two variables that are related to each other by an invertible function. While it has previously been shown that the dependence structure of the noise can provide hints to determine which of the two variables is the cause, we presently show that even in the deterministic (noise-free) case, there are asymmetries that can be exploited for causal inference. Our method is based on the idea that if the function and the probability density of the cause are chosen independently, then the distribution of the effect will, in a certain sense, depend on the function. We provide a theoretical analysis of this method, showing that it also works in the low noise regime, and link it to information geometry. We report strong empirical results on various real-world data sets from different domains.

Povilas Daniusis, Dominik Janzing, Joris Mooij, Jakob Zscheischler, Bastian Steudel, Kun Zhang, Bernhard Schoelkopf · 2012

Related benchmarks

Task	Dataset	Result
Bivariate Causal Discovery	PER	Accuracy100	33
Bivariate Causal Discovery	LS	Accuracy95	33
Bivariate Causal Discovery	Tue	Accuracy62	33
Bivariate Causal Discovery	AN	Accuracy89	33
Bivariate Causal Discovery	NN-V	Accuracy48	33
Bivariate Causal Discovery	Net	Accuracy57	33
Bivariate Causal Discovery	Qd-V	Accuracy47	33
Bivariate Causal Discovery	D4 s1	Accuracy42	33
Bivariate Causal Discovery	SIM	Accuracy36	33
Bivariate Causal Discovery	SIM-c	Accuracy42	33