Redundant variables and Granger causality

Abstract

We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under-estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

4 pages, 5 figures. Accepted for publication in Physical Review E

References in corpus (4)

• Kernel method for nonlinear Granger causality
• Estimating Granger causality from Fourier and wavelet transforms of time series data
• Kernel Granger causality and the analysis of dynamical networks
• Identification of functional information subgraphs in complex networks

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