Conditional distribution variability measures for causality detection

About

In this paper we derive variability measures for the conditional probability distributions of a pair of random variables, and we study its application in the inference of causal-effect relationships. We also study the combination of the proposed measures with standard statistical measures in the the framework of the ChaLearn cause-effect pair challenge. The developed model obtains an AUC score of 0.82 on the final test database and ranked second in the challenge.

José A. R. Fonollosa · 2016

Related benchmarks

Task	Dataset	Result
Causal Discovery	Tübingen	AUROC59	37
Bivariate Causal Discovery	Tue	Accuracy67	33
Bivariate Causal Discovery	Qd-V	Accuracy78	33
Bivariate Causal Discovery	SIM-c	Accuracy76	33
Bivariate Causal Discovery	SIM	Accuracy71	33
Bivariate Causal Discovery	Net	Accuracy78	33
Bivariate Causal Discovery	D4 s1	Accuracy58	33
Bivariate Causal Discovery	LS	Accuracy76	33
Bivariate Causal Discovery	AN	Accuracy99	33
Bivariate Causal Discovery	NN-V	Accuracy52	33