Causal inference with multiple time series: principles and problems

doi:10.1098/rsta.2011.0613

Causal inference with multiple time series: principles and problems

2013-08-28

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Michael Eichler *

DOI：10.1098/rsta.2011.0613

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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 nontechnical and thus accessible to applied scientists who are interested in adopting the method.

Keywords:

causal identification

Granger causality

causal effect

spurious causality

latent variables

impulse response function

Causal inference with multiple time series: principles and problems

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