Physics and MathematicsNo Access

INFORMATION FLOWS IN CAUSAL NETWORKS

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

and

Algorithms and Adaptive Systems Research Groups, School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

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https://doi.org/10.1142/S0219525908001465Cited by:177 (Source: Crossref)

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.

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