Quantifying 'causality' in complex systems: Understanding Transfer Entropy

TL;DR

This paper evaluates the effectiveness of Transfer Entropy in identifying causal relationships in complex systems using models like the Ising model and a Random Transition model, focusing on data size effects.

Contribution

It systematically assesses Transfer Entropy's reliability for causality detection in complex systems with stochastic fluctuations and finite data.

Findings

Transfer Entropy can identify causal relations in complex models.

Finite data size impacts the reliability of Transfer Entropy.

Stochastic fluctuations influence causality detection accuracy.

Abstract

'Causal' direction is of great importance when dealing with complex systems. Often big volumes of data in the form of time series are available and it is important to develop methods that can inform about possible causal connections between the different observables. Here we investigate the ability of the Transfer Entropy measure to identify causal relations embedded in emergent coherent correlations. We do this by firstly applying Transfer Entropy to an amended Ising model. In addition we use a simple Random Transition model to test the reliability of Transfer Entropy as a measure of `causal' direction in the presence of stochastic fluctuations. In particular we systematically study the effect of the finite size of data sets.