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.
|Number of pages||17|
|Journal||Philosophical Transactions of the Royal Society A: mathematical Physical and Engineering Sciences|
|Publication status||Published - 1 Jan 2013|