Proceedings of the 17th International Conference on Artificial Neural Networks vol:17 pages:159-168
International Conference on Artificial Neural Networks edition:17 location:Porto, Portugal date:9-13 September
As a causality criterion we propose the conditional relative entropy. The relationship with information theoretic functionals mutual information and entropy is established. The conditional relative entropy criterion is compared with 3 well-established techniques for causality detection: ‘Sims’, ‘Geweke-Meese-Dent’ and ‘Granger’. It is shown that the conditional relative entropy, as opposed to these 3 criteria, is sensitive to non-linear causal relationships. All results are illustrated on real-world time series of human gait.