Nonlinear extensions of new causality

New causality (NC) is a relatively recent method for inferring causal relationships between time-series data. Similar to other popular causal inference methods, like Granger causality (GC), NC can be evaluated in time or frequency domain. NC derives its value by partitioning a predictive model, grou...

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Autores principales:	Pedro C. Nariyoshi, J.R. Deller, Jr.
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Nonlinear modeling New causality Granger causality Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/6bcdca33cd5149e0b8ed61fa555b378c
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id	oai:doaj.org-article:6bcdca33cd5149e0b8ed61fa555b378c
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spelling	oai:doaj.org-article:6bcdca33cd5149e0b8ed61fa555b378c2021-12-03T04:01:36ZNonlinear extensions of new causality2772-528610.1016/j.neuri.2021.100001https://doaj.org/article/6bcdca33cd5149e0b8ed61fa555b378c2021-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2772528621000017https://doaj.org/toc/2772-5286New causality (NC) is a relatively recent method for inferring causal relationships between time-series data. Similar to other popular causal inference methods, like Granger causality (GC), NC can be evaluated in time or frequency domain. NC derives its value by partitioning a predictive model, grouping them by different inputs, and finding a normalized ratio of the power of all contributions. In its seminal form, NC is defined atop a linear ARMAX models. If the contribution between two time-series cannot be accurately expressed with a linear model, the seminal form of NC cannot accurately measure the causal relationship. In the frequency domain, linear models also prevent cross-frequency contributions from being measured. This work introduces an extension to NC to NARMAX models. This extension reduces to the seminal form when applied to linear models and can be also evaluated in the frequency domain. The nonlinear extension is applied to a range of synthetic models and real EEG data with promising results. A discussion on modeling and its effect on linear and nonlinear NC estimates is provided.Pedro C. NariyoshiJ.R. Deller, Jr.ElsevierarticleNonlinear modelingNew causalityGranger causalityNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroscience Informatics, Vol 1, Iss 1, Pp 100001- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Nonlinear modeling New causality Granger causality Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Nonlinear modeling New causality Granger causality Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Pedro C. Nariyoshi J.R. Deller, Jr. Nonlinear extensions of new causality
description	New causality (NC) is a relatively recent method for inferring causal relationships between time-series data. Similar to other popular causal inference methods, like Granger causality (GC), NC can be evaluated in time or frequency domain. NC derives its value by partitioning a predictive model, grouping them by different inputs, and finding a normalized ratio of the power of all contributions. In its seminal form, NC is defined atop a linear ARMAX models. If the contribution between two time-series cannot be accurately expressed with a linear model, the seminal form of NC cannot accurately measure the causal relationship. In the frequency domain, linear models also prevent cross-frequency contributions from being measured. This work introduces an extension to NC to NARMAX models. This extension reduces to the seminal form when applied to linear models and can be also evaluated in the frequency domain. The nonlinear extension is applied to a range of synthetic models and real EEG data with promising results. A discussion on modeling and its effect on linear and nonlinear NC estimates is provided.
format	article
author	Pedro C. Nariyoshi J.R. Deller, Jr.
author_facet	Pedro C. Nariyoshi J.R. Deller, Jr.
author_sort	Pedro C. Nariyoshi
title	Nonlinear extensions of new causality
title_short	Nonlinear extensions of new causality
title_full	Nonlinear extensions of new causality
title_fullStr	Nonlinear extensions of new causality
title_full_unstemmed	Nonlinear extensions of new causality
title_sort	nonlinear extensions of new causality
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/6bcdca33cd5149e0b8ed61fa555b378c
work_keys_str_mv	AT pedrocnariyoshi nonlinearextensionsofnewcausality AT jrdellerjr nonlinearextensionsofnewcausality
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Nonlinear extensions of new causality

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