Nonparametric estimation of information causality between analog narrowband signals

Abstract

In this paper we address the dimensionality problem regarding information causality estimation and propose a new method to approximate directed information by identifying and eliminating superfluous calculations. Using only the most informational components we reduce conditional mutual information terms while capturing all the instantaneous or lagged dependencies between stochastic signals. Our method is tested on simulated analog narrowband signals with varying causal relationships.