Department of Quantitative Economics, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.
Philos Trans A Math Phys Eng Sci. 2013 Jul 15;371(1997):20110613. doi: 10.1098/rsta.2011.0613. Print 2013 Aug 28.
I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.
我回顾了格兰杰因果关系概念在时间序列数据因果推断中的应用。首先，我通过将该概念与其他理论因果度量联系起来，从理论上证明了其合理性。其次，我概述了虚假因果关系可能存在的问题以及解决这些问题的方法。最后，我概述了一种在存在潜在变量的情况下学习因果时间序列结构的识别算法。该算法的描述是非技术性的，因此对有兴趣采用该方法的应用科学家来说是可以理解的。
