标题： 因果信息方法在多变量数据集部分条件中的应用 显示英文标题

标题： Causal information approach to partial conditioning in multivariate data sets

摘要： 在评估多变量数据集中一个时间序列对另一个时间序列的因果影响时，必须考虑其他变量的条件效应。 在存在许多变量且可能样本数量较少的情况下，完全条件化可能导致计算和数值问题。 本文在信息论的框架下，研究了对有限变量子集进行部分条件化的問題。 所提出的方法在模拟数据集和癫痫患者颅内脑电记录的一个例子上进行了测试。 我们表明，在许多情况下，对为驱动节点选择的少量变量进行条件化，可以得到与完全多变量分析结果非常接近的结果，甚至在样本数量较少的情况下效果更好。 这在因果模式稀疏时尤其相关。 显示英文摘要

摘要： 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.