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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ftcsic:oai:digital.csic.es:10261/123551 2024-02-11T10:04:47+01:00 Distinguishing time-delayed causal interactions using convergent cross mapping Ye, Hao Deyle, Ethan R. Gilarranz, Luis J. Sugihara, george 2015 http://hdl.handle.net/10261/123551 https://doi.org/10.1038/srep14750 en eng Nature Publishing Group Publisher's version http://dx.doi.org/10.1038/srep14750 Sí Scientific Reports, 5:14750 %7C(2015) http://hdl.handle.net/10261/123551 doi:10.1038/srep14750 26435402 open artículo http://purl.org/coar/resource_type/c_6501 2015 ftcsic https://doi.org/10.1038/srep14750 2024-01-16T10:11:12Z 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 longterm 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 Peer reviewed Article in Journal/Newspaper ice core Digital.CSIC (Spanish National Research Council) Scientific Reports 5 1 |
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Digital.CSIC (Spanish National Research Council) |
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ftcsic |
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English |
description |
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 longterm 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 Peer reviewed |
format |
Article in Journal/Newspaper |
author |
Ye, Hao Deyle, Ethan R. Gilarranz, Luis J. Sugihara, george |
spellingShingle |
Ye, Hao Deyle, Ethan R. Gilarranz, Luis J. Sugihara, george Distinguishing time-delayed causal interactions using convergent cross mapping |
author_facet |
Ye, Hao Deyle, Ethan R. Gilarranz, Luis J. Sugihara, george |
author_sort |
Ye, Hao |
title |
Distinguishing time-delayed causal interactions using convergent cross mapping |
title_short |
Distinguishing time-delayed causal interactions using convergent cross mapping |
title_full |
Distinguishing time-delayed causal interactions using convergent cross mapping |
title_fullStr |
Distinguishing time-delayed causal interactions using convergent cross mapping |
title_full_unstemmed |
Distinguishing time-delayed causal interactions using convergent cross mapping |
title_sort |
distinguishing time-delayed causal interactions using convergent cross mapping |
publisher |
Nature Publishing Group |
publishDate |
2015 |
url |
http://hdl.handle.net/10261/123551 https://doi.org/10.1038/srep14750 |
genre |
ice core |
genre_facet |
ice core |
op_relation |
Publisher's version http://dx.doi.org/10.1038/srep14750 Sí Scientific Reports, 5:14750 %7C(2015) http://hdl.handle.net/10261/123551 doi:10.1038/srep14750 26435402 |
op_rights |
open |
op_doi |
https://doi.org/10.1038/srep14750 |
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Scientific Reports |
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5 |
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1 |
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1790601514315677696 |