Causal information approach to partial conditioning in multivariate data sets

TL;DR

This paper introduces a causal information approach for partial conditioning in multivariate data, effectively reducing computational issues and improving causal inference accuracy in sparse causal networks.

Contribution

It proposes a novel information-theoretic method for partial conditioning on a subset of variables, demonstrating its effectiveness on simulated and real EEG data.

Findings

Partial conditioning yields results close to full analysis in many cases.

Selecting the most informative variables improves causal inference with limited samples.

Method performs well on intracranial EEG data from epileptic subjects.

Abstract

When evaluating causal influence from one time series to another in a multivariate dataset 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 datasets 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.