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Granger causality and transfer entropy are equivalent for Gaussian variables
journal contribution
posted on 2023-06-07, 20:52 authored by Lionel BarnettLionel Barnett, Adam BarrettAdam Barrett, Anil SethAnil SethGranger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.
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Publication status
- Published
Journal
Physical Review LettersISSN
1079-7114External DOI
Issue
23Volume
103Page range
238701-1Department affiliated with
- Informatics Publications
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- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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