Causal information approach to partial conditioning in multivariate data sets.
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.
Department of Data Analysis, Faculty of Psychology and Pedagogical Sciences, University of Gent, 9000 Gent, Belgium. daniele.marinazzo@ugent.be
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D015984:Causality; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004569:Electroencephalography / Q000706:statistics & numerical data; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D007257:Information Theory; D016014:Linear Models; D015999:Multivariate Analysis