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Causality in Reversed Time Series: Reversed or Conserved?

The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account...

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Autores principales: Kořenek, Jakub, Hlinka, Jaroslav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391759/
https://www.ncbi.nlm.nih.gov/pubmed/34441207
http://dx.doi.org/10.3390/e23081067
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author Kořenek, Jakub
Hlinka, Jaroslav
author_facet Kořenek, Jakub
Hlinka, Jaroslav
author_sort Kořenek, Jakub
collection PubMed
description The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed.
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spelling pubmed-83917592021-08-28 Causality in Reversed Time Series: Reversed or Conserved? Kořenek, Jakub Hlinka, Jaroslav Entropy (Basel) Article The inference of causal relations between observable phenomena is paramount across scientific disciplines; however, the means for such enterprise without experimental manipulation are limited. A commonly applied principle is that of the cause preceding and predicting the effect, taking into account other circumstances. Intuitively, when the temporal order of events is reverted, one would expect the cause and effect to apparently switch roles. This was previously demonstrated in bivariate linear systems and used in design of improved causal inference scores, while such behaviour in linear systems has been put in contrast with nonlinear chaotic systems where the inferred causal direction appears unchanged under time reversal. The presented work explores the conditions under which the causal reversal happens—either perfectly, approximately, or not at all—using theoretical analysis, low-dimensional examples, and network simulations, focusing on the simplified yet illustrative linear vector autoregressive process of order one. We start with a theoretical analysis that demonstrates that a perfect coupling reversal under time reversal occurs only under very specific conditions, followed up by constructing low-dimensional examples where indeed the dominant causal direction is even conserved rather than reversed. Finally, simulations of random as well as realistically motivated network coupling patterns from brain and climate show that level of coupling reversal and conservation can be well predicted by asymmetry and anormality indices introduced based on the theoretical analysis of the problem. The consequences for causal inference are discussed. MDPI 2021-08-17 /pmc/articles/PMC8391759/ /pubmed/34441207 http://dx.doi.org/10.3390/e23081067 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kořenek, Jakub
Hlinka, Jaroslav
Causality in Reversed Time Series: Reversed or Conserved?
title Causality in Reversed Time Series: Reversed or Conserved?
title_full Causality in Reversed Time Series: Reversed or Conserved?
title_fullStr Causality in Reversed Time Series: Reversed or Conserved?
title_full_unstemmed Causality in Reversed Time Series: Reversed or Conserved?
title_short Causality in Reversed Time Series: Reversed or Conserved?
title_sort causality in reversed time series: reversed or conserved?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391759/
https://www.ncbi.nlm.nih.gov/pubmed/34441207
http://dx.doi.org/10.3390/e23081067
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