Conditional distribution variability measures for causality detection
Machine Learning
2016-01-26 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.1601.06680,
title = {Conditional distribution variability measures for causality detection},
author = {José A. R. Fonollosa},
journal= {arXiv preprint arXiv:1601.06680},
year = {2016}
}
Comments
NIPS 2013 workshop on causality