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MiS Preprint

Information Flows in Causal Networks

Nihat Ay and Daniel Polani


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.

MSC Codes:
94A15, 94A40, 94B15
information flow, causality, directed acyclic graphs, mutual information

Related publications

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