Granger causality and transfer entropy are equivalent for Gaussian variables
TLDR
It is shown that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.Abstract:
Granger 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.read more
Citations
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Journal ArticleDOI
The MVGC Multivariate Granger Causality Toolbox: A New Approach to Granger-causal Inference
Lionel Barnett,Anil K. Seth +1 more
TL;DR: The theoretical basis, computational strategy and application to empirical G-causality inference of the MVGC Toolbox are explained and the advantages of the Toolbox over previous methods in terms of computational accuracy and statistical inference are shown.
Journal ArticleDOI
A MATLAB toolbox for Granger causal connectivity analysis
TL;DR: A freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals.
Journal ArticleDOI
Wiener-Granger causality: a well established methodology.
Steven L. Bressler,Anil K. Seth +1 more
TL;DR: This article describes a fundamentally different approach to identifying causal connectivity in neuroscience: a focus on the predictability of ongoing activity in one part from that in another, made possible by a new method that comes from the pioneering work of Wiener (1956) and Granger (1969).
Journal ArticleDOI
Granger Causality Analysis in Neuroscience and Neuroimaging
TL;DR: Granger causality (G-causality) analysis is used for the characterization of functional circuits underpinning perception, cognition, behavior, and consciousness in neuroscience.
Journal ArticleDOI
Analysing connectivity with Granger causality and dynamic causal modelling.
TL;DR: An overview of advances in Granger causality and dynamic causal modelling and a comparative evaluation of both approaches in terms of their pros and cons are presented.
References
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