While studying complex systems, one of the fundamental questions is to identify causal relationships (i.e., which system drives which) between relevant subsystems. In this paper, we focus on information-theoretic approaches for causality detection by means of directionality index based on mutual information estimation. We briefly review the current methods for mutual information estimation from the point of view of their consistency. We also present some arguments from recent literature, supporting the usefulness of the information-theoretic tools for causality detection. © 2009 Springer US.
CITATION STYLE
Hlavackova-Schindler, K. (2009). Causality in time series: Its detection and quantification by means of information theory. In Information Theory and Statistical Learning (pp. 183–207). Springer US. https://doi.org/10.1007/978-0-387-84816-7_8
Mendeley helps you to discover research relevant for your work.