Conditional distribution variability measures for causality detection

2016/01/25 by Fonollosa, José A. R. · 2 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · doi:10.48550/arxiv.1601.06680

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.

Cited by

• LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classes
• From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery

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