INFORMATION FLOWS IN CAUSAL NETWORKS

Nihat Ay () and Daniel Polani ()
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Nihat Ay: Max Planck Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103 Leipzig, Germany; Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501, USA
Daniel Polani: Algorithms and Adaptive Systems Research Groups, School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

Advances in Complex Systems (ACS), 2008, vol. 11, issue 01, pages 17-41

Abstract: We use a notion of causal independence based on intervention, which is a fundamental concept of the theory of causal networks, to define a measure for the strength of a causal effect. We call this measure "information flow" and compare it with known information flow measures such as transfer entropy.

Keywords: Causality; information theory; information flow; Bayesian networks (search for similar items in EconPapers)
Date: 2008
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