On directed information theory and Granger causality graphs

TL;DR

This paper explores how directed information theory can be extended to networks for assessing Granger causality, providing a comprehensive framework that includes transfer entropy, with applications in neuroscience connectivity inference.

Contribution

It introduces an extension of directed information theory to networks, demonstrating its suitability for Granger causality analysis and neuroscience applications.

Findings

Directed information encompasses transfer entropy measures.

The framework effectively assesses Granger causality in stochastic processes.

Applicable to neuroscience connectivity inference.

Abstract

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.