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We have decided to discontinue the publication of preprints on our preprint server end of 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.
Information Flows in Causal Networks
Nihat Ay and Daniel PolaniAbstract
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:
- 02.05.06
- Published:
- 02.05.06
- MSC Codes:
- 94A15, 94A40, 94B15
- Keywords:
- information flow, causality, directed acyclic graphs, mutual information