We propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach systematically quantifies the causality among their different system components rather than merely analyze systems as entireties. The generated spectrum offers a rich-information representation of time-varying latent causal relations, efficiently dealing with non-stationary processes and high-dimensional conditions. We demonstrate its validity in the aspects of parameter dependence, statistic significance test, and sensibility. An open-source multi-platform implementation of this metric is developed and computationally applied on neuroscience data sets and diffusively coupled logistic oscillators.

翻译：我们建议使用Fourier-domemain转移星系,这是对转移星系的一种新颖的概括化,作为无因果关系的示范指标。对于任意性系统,这种方法系统地量化其不同系统组成部分的因果关系,而不仅仅是将系统分析成整体。生成星系可以提供丰富信息,说明时间变化的潜在因果关系,有效地处理非静止过程和高维条件。我们在参数依赖性、统计意义测试和感知性等方面证明了该星系的有效性。该星系的开放源多平台实施开发并计算适用于神经科学数据集和多源组合后勤振荡器。