Transfer Entropy

Abstract

In many research fields, we need to analyze the causal interactions among the variables of a complex system, to better understand the physical behavior of it. While linear techniques, such as correlation, are widely used to identify and characterize these relationships, more advanced information-based techniques, such as mutual information and transfer entropy, have proven to be superior. Transfer entropy has been used to analyze the causal relationships between subsystem variables from data. The fact that it is non-symmetric enables one to infer the direction of information flow. Granger causality, which generally relies on autoregression to assess interactions, has been shown to be equivalent to transfer entropy in the case of Gaussian variables.