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Inference of causality is central in nonlinear time series analysis andscience in general. A popular approach to infer causality between two processesis to measure the information flow between them in terms of transfer entropy.Using dynamics of coupled oscillator networks, we show that although transferentropy can successfully detect information flow in two processes, it oftenresults in erroneous identification of network connections under the presenceof indirect interactions, dominance of neighbors, or anticipatory couplings.Such effects are found to be profound for time-dependent networks. To overcomethese limitations, we develop a measure called causation entropy and show thatits application can lead to reliable identification of true couplings.

Causation Entropy Identifies Indirect Influences, Dominance of Neighbors and Anticipatory Couplings