Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables
Granger Causality and Transfer Entropy Are Equivalent for Gaussian Variables
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DOI：
10.1103/physrevlett.103.238701
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发表时间：
2009-12-04
影响因子：
8.6
通讯作者：
Seth, Anil K.
中科院分区：
文献类型：
--
作者：
Barnett, Lionel;Barrett, Adam B.;Seth, Anil K.
格兰杰因果关系是基于向量自回归预测的因果影响的统计概念。它最初是在计量经济学领域发展起来的，后来在更广泛的竞技场中得到了应用，特别是在神经科学中。最近，转移熵，一个信息理论的测量共同依赖的过程之间的时间导向的信息传递，在类似的广泛领域获得了牵引力。虽然人们认识到这两个概念必须相互关联，但确切的关系迄今尚未得到正式描述。在这里，我们表明，高斯变量，格兰杰因果关系和转移熵是完全等价的，从而桥接自回归和信息理论的方法，以数据驱动的因果推理。
Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.