New method for detecting causality
Sugihara, G., May, R., Ye, H., Hsieh, C.H., Deyle, E., Fogarty, M. & Munch, S. (2012) Detecting causality in complex ecosystems. Science 338 (496):496-500 [Full text]
Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. Here we introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.
The method is explained in three short videos:
Time series and dynamical systems:
Takens' theorem and shadow manifolds:
Convergent cross mapping: