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Inferring causality in networks of WSS processes by pairwise estimation methods

机译：通过成对估计方法推断WSS流程网络中的因果关系

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Inferring causal dependences in a family of dynamic systems from a finite set of observations is a problem encountered in many applications that arise in a diverse variety of fields; ranging from economics and finance to climatology and neuroscience. Given a set of random processes, the objective is to determine whether one process is influenced by the others and to investigate the nature of this influence in case a dependence relation is identified. The notion of Granger-causality may be used in this context to measure and quantify causal structures. Ideally, in order to infer the complete interdependence structure of a complex system, one should simultaneously consider the dynamic behaviour of all the processes involved. However, for large networks, such a method becomes exceedingly complicated. In this paper, we consider an interdependent group of jointly wide sense stationary real-valued stochastic processes and investigate the problem of determining Granger-causality by identifying pairwise causal relations. It is seen that while such methods may not reveal all details of a system, they can nonetheless provide useful and reasonably accurate information.
机译：从一组有限的观察结果推断动态系统族中的因果相关性是在各种领域中出现的许多应用中遇到的一个问题。从经济学和金融学到气候学和神经科学，应有尽有。给定一组随机过程，目标是确定一个过程是否受其他过程影响，并在确定依赖关系的情况下调查这种影响的性质。在这种情况下，可以使用格兰杰因果关系的概念来度量和量化因果结构。理想情况下，为了推断复杂系统的完整相互依赖结构，应该同时考虑所有涉及过程的动态行为。但是，对于大型网络，这种方法变得极其复杂。在本文中，我们考虑了相互依赖的一组广义广义平稳实值随机过程，并研究了通过识别成对因果关系来确定格兰杰因果关系的问题。可以看出，尽管这样的方法可能无法揭示系统的所有细节，但它们仍然可以提供有用且合理的信息。

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