Affiliation:
1. Department of Quantitative Economics, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands
Abstract
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.
Subject
General Physics and Astronomy,General Engineering,General Mathematics
Cited by
84 articles.
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