Computational Intelligence approaches to causality detection

Discovering interdependencies and causal relationships is one of the most relevant challenges raised by the information era. As more and better data become available, there is an urgent need for data-driven techniques with the capability of efficiently detecting hidden interactions. As such, this important issue is receiving increasing attention in the recent literature. The aim of the Learning Causality Special Session is to bring together theory-oriented and practitioners of this fascinating discipline. The main streams of causality detection by Computational Intelligence will be covered, namely, the probabilistic, information-theoretic, and Granger approaches.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Katerina Hlavácková-Schindler, Pablo F. Verdes
• Keywords: Causal Relationships, Causality Detection, Causality Special Session, ESANN 2007, Neural Networks