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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 303601, 8 pages
Research Article

Causal Information Approach to Partial Conditioning in Multivariate Data Sets

1Department of Data Analysis, Faculty of Psychology and Pedagogical Sciences, University of Gent, 9000 Gent, Belgium
2Dipartimento Interateneo di Fisica “Michelangelo Merlin”, University of Bari, 70126 Bari, Italy
3TIRES-Center of Innovative Technologies for Signal Detection and Processing, University of Bari, 70125 Bari, Italy
4INFN, Sezione di Bari, 70125 Bari, Italy

Received 2 November 2011; Revised 15 March 2012; Accepted 18 March 2012

Academic Editor: Dimitris Kugiumtzis

Copyright © 2012 D. Marinazzo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


When evaluating causal influence from one time series to another in a multivariate data set it is necessary to take into account the conditioning effect of the other variables. In the presence of many variables and possibly of a reduced number of samples, full conditioning can lead to computational and numerical problems. In this paper, we address the problem of partial conditioning to a limited subset of variables, in the framework of information theory. The proposed approach is tested on simulated data sets and on an example of intracranial EEG recording from an epileptic subject. We show that, in many instances, conditioning on a small number of variables, chosen as the most informative ones for the driver node, leads to results very close to those obtained with a fully multivariate analysis and even better in the presence of a small number of samples. This is particularly relevant when the pattern of causalities is sparse.