Towards a framework for observational causality from time series: when Shannon meets Turing
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A tensor based formalism is proposed for inferring causal structures. This formalism enables us to determine the directionality of relations within a complex network. It furthermore allows us to differentiate between direct and indirect associations. Using this framework a Data Processing Inequality is proved to exist for Transfer Entropy.
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