title: Causal inference with multiple time series: principles and problems.
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.
authors.affiliation: Department of Quantitative Economics, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands. m.eichler@maastrichtuniversity.nl
authors.forename: Michael
authors.initials: M
authors.lastname: Eichler
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical
keywords: Granger causality; causal effect; causal identification; impulse response function; latent variables; spurious causality