Distinguishing time-delayed causal interactions using convergent cross mapping

By H. Ye, Ethan R. Deyle, Luis J. Gilarranz, George Sugihara. Scientific Reports, 2015

434 citations recorded by the source corpus.

An important problem across many scientific fields is the identification of causal effects from observational data alone. Recent methods (convergent cross mapping, CCM) have made substantial progress on this problem by applying the idea of nonlinear attractor reconstruction to time series data. Here, we expand upon the technique of CCM by explicitly considering time lags. Applying this extended method to representative examples (model simulations, a laboratory predator-prey experiment, temperature and greenhouse gas reconstructions from the Vostok ice core and long-term ecological time series collected in the Southern California Bight), we demonstrate the ability to identify different time-delayed interactions, distinguish between synchrony induced by strong unidirectional-forcing and true bidirectional causality and resolve transitive causal chains.

Subfields: Artificial Intelligence, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/58ee1b80de7d40c969d0376cfc6955529741a17a

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Citations in the corpus

• D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery (2021, methodology): “’s Convergent Cross Mapping methods [232, 269].”