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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
47/2006

Information Flows in Causal Networks

Nihat Ay and Daniel Polani

Abstract

We introduce a notion of causal independence based on virtual intervention, which is a fundamental concept of the theory of causal networks. Causal independence allows for defining a measure for the strength of a causal effect. We call this information flow and compare it with known information flow measures such as the transfer entropy.

Received:
May 2, 2006
Published:
May 2, 2006
MSC Codes:
94A15, 94A40, 94B15
Keywords:
information flow, causality, directed acyclic graphs, mutual information

Related publications

inJournal
2008 Repository Open Access
Nihat Ay and Daniel Polani

Information flows in causal networks

In: Advances in complex systems, 11 (2008) 1, pp. 17-41