Distinguishing time-delayed causal interactions using convergent cross mapping

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...

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Bibliographic Details
Published inScientific reports Vol. 5; no. 1; p. 14750
Main Authors Ye, Hao, Deyle, Ethan R., Gilarranz, Luis J., Sugihara, George
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 05.10.2015
Nature Publishing Group
Subjects
631/158/1745
639/766/530/2803
704/106/413
Greenhouse gases
Humanities and Social Sciences
Ice
Mapping
multidisciplinary
Prey
Science
Temperature effects
Time series
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/srep14750