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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ftbostonuniv:oai:open.bu.edu:2144/47137 2023-11-12T04:18:36+01:00 Distinguishing time-delayed causal interactions using convergent cross mapping Ye, Hao Deyle, Ethan R. Gilarranz, Luis J. Sugihara, George England 2015-10-05 14750- Electronic https://hdl.handle.net/2144/47137 https://www.ncbi.nlm.nih.gov/pubmed/26435402 https://doi.org/10.1038/srep14750 eng en eng Springer Science and Business Media LLC Scientific Reports https://www.ncbi.nlm.nih.gov/pubmed/26435402 http://dx.doi.org/10.1038/srep14750 H. Ye, E.R. Deyle, L.J. Gilarranz, G. Sugihara. 2015. "Distinguishing time-delayed causal interactions using convergent cross mapping." Scientific Reports, Volume 5, Issue 1, pp.14750-. https://doi.org/10.1038/srep14750 2045-2322 https://hdl.handle.net/2144/47137 doi:10.1038/srep14750 0000-0001-8704-8434 (Deyle, Ethan R) 804796 This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ Article 2015 ftbostonuniv https://doi.org/10.1038/srep14750 2023-10-28T22:19:05Z 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. Published version Article in Journal/Newspaper ice core Boston University: OpenBU Scientific Reports 5 1 |
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Boston University: OpenBU |
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ftbostonuniv |
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English |
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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. Published version |
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 |
Springer Science and Business Media LLC |
publishDate |
2015 |
url |
https://hdl.handle.net/2144/47137 https://www.ncbi.nlm.nih.gov/pubmed/26435402 https://doi.org/10.1038/srep14750 |
op_coverage |
England |
genre |
ice core |
genre_facet |
ice core |
op_relation |
Scientific Reports https://www.ncbi.nlm.nih.gov/pubmed/26435402 http://dx.doi.org/10.1038/srep14750 H. Ye, E.R. Deyle, L.J. Gilarranz, G. Sugihara. 2015. "Distinguishing time-delayed causal interactions using convergent cross mapping." Scientific Reports, Volume 5, Issue 1, pp.14750-. https://doi.org/10.1038/srep14750 2045-2322 https://hdl.handle.net/2144/47137 doi:10.1038/srep14750 0000-0001-8704-8434 (Deyle, Ethan R) 804796 |
op_rights |
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ |
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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1782335206000164864 |