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When evaluating causal influence from one time series to another in amultivariate dataset it is necessary to take into account the conditioningeffect of the other variables. In the presence of many variables, and possiblyof a reduced number of samples, full conditioning can lead to computational andnumerical problems. In this paper we address the problem of partialconditioning to a limited subset of variables, in the framework of informationtheory. The proposed approach is tested on simulated datasets and on an exampleof intracranial EEG recording from an epileptic subject. We show that, in manyinstances, conditioning on a small number of variables, chosen as the mostinformative ones for the driver node, leads to results very close to thoseobtained with a fully multivariate analysis, and even better in the presence ofa small number of samples. This is particularly relevant when the pattern ofcausalities is sparse.

Causal information approach to partial conditioning in multivariate data sets