Partial cross mapping eliminates indirect causal influences

It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world da...

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Autores principales:	Siyang Leng, Huanfei Ma, Jürgen Kurths, Ying-Cheng Lai, Wei Lin, Kazuyuki Aihara, Luonan Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/e72498840e1b4a95b6f0ce9021512759
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spelling	oai:doaj.org-article:e72498840e1b4a95b6f0ce90215127592021-12-02T15:49:49ZPartial cross mapping eliminates indirect causal influences10.1038/s41467-020-16238-02041-1723https://doaj.org/article/e72498840e1b4a95b6f0ce90215127592020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16238-0https://doaj.org/toc/2041-1723It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data.Siyang LengHuanfei MaJürgen KurthsYing-Cheng LaiWei LinKazuyuki AiharaLuonan ChenNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Siyang Leng Huanfei Ma Jürgen Kurths Ying-Cheng Lai Wei Lin Kazuyuki Aihara Luonan Chen Partial cross mapping eliminates indirect causal influences
description	It is crucial yet challenging to identify cause-consequence relation in complex dynamical systems where direct causal links can mix with indirect ones. Leng et al. propose a data-driven model-independent method to distinguish direct from indirect causality and test its applicability to real-world data.
format	article
author	Siyang Leng Huanfei Ma Jürgen Kurths Ying-Cheng Lai Wei Lin Kazuyuki Aihara Luonan Chen
author_facet	Siyang Leng Huanfei Ma Jürgen Kurths Ying-Cheng Lai Wei Lin Kazuyuki Aihara Luonan Chen
author_sort	Siyang Leng
title	Partial cross mapping eliminates indirect causal influences
title_short	Partial cross mapping eliminates indirect causal influences
title_full	Partial cross mapping eliminates indirect causal influences
title_fullStr	Partial cross mapping eliminates indirect causal influences
title_full_unstemmed	Partial cross mapping eliminates indirect causal influences
title_sort	partial cross mapping eliminates indirect causal influences
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e72498840e1b4a95b6f0ce9021512759
work_keys_str_mv	AT siyangleng partialcrossmappingeliminatesindirectcausalinfluences AT huanfeima partialcrossmappingeliminatesindirectcausalinfluences AT jurgenkurths partialcrossmappingeliminatesindirectcausalinfluences AT yingchenglai partialcrossmappingeliminatesindirectcausalinfluences AT weilin partialcrossmappingeliminatesindirectcausalinfluences AT kazuyukiaihara partialcrossmappingeliminatesindirectcausalinfluences AT luonanchen partialcrossmappingeliminatesindirectcausalinfluences
_version_	1718385723614167040

Partial cross mapping eliminates indirect causal influences

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