Understanding causation via linear response theory

In spite of the (correct) common-wisdom statement correlation does not imply causation, a proper employ of time correlations and of fluctuation-response theory allows us to understand the causal relations between the variables of a multidimensional linear Markov process. It is shown that the fluctuation-response formalism can be used both to find the direct causal links between the variables of a system and to introduce a degree of causation, cumulative in time, whose physical interpretation is straightforward. Although for generic nonlinear dynamics there is no simple exact relationship between correlations and response functions, the described protocol can still give a useful proxy also in the presence of weak nonlinear terms. This approach can be used for a critical analysis of paleoclimate data.