Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples
Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with...
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ftrepec:oai:RePEc:plo:pone00:0158572 2023-05-15T16:39:14+02:00 Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158572 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158572&type=printable unknown https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158572 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158572&type=printable article ftrepec 2020-12-04T13:33:35Z Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly. Article in Journal/Newspaper ice core RePEc (Research Papers in Economics) |
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Identifying causal relations from time series is the first step to understanding the behavior of complex systems. Although many methods have been proposed, few papers have applied multiple methods together to detect causal relations based on time series generated from coupled nonlinear systems with some unobserved parts. Here we propose the combined use of three methods and a majority vote to infer causality under such circumstances. Two of these methods are proposed here for the first time, and all of the three methods can be applied even if the underlying dynamics is nonlinear and there are hidden common causes. We test our methods with coupled logistic maps, coupled Rössler models, and coupled Lorenz models. In addition, we show from ice core data how the causal relations among the temperature, the CH4 level, and the CO2 level in the atmosphere changed in the last 800,000 years, a conclusion also supported by irregularly sampled data analysis. Moreover, these methods show how three regions of the brain interact with each other during the visually cued, two-choice arm reaching task. Especially, we demonstrate that this is due to bottom up influences at the beginning of the task, while there exist mutual influences between the posterior medial prefrontal cortex and the presupplementary motor area. Based on our results, we conclude that identifying causality with an appropriate ensemble of multiple methods ensures the validity of the obtained results more firmly. |
format |
Article in Journal/Newspaper |
author |
Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara |
spellingShingle |
Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
author_facet |
Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara |
author_sort |
Yoshito Hirata |
title |
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
title_short |
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
title_full |
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
title_fullStr |
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
title_full_unstemmed |
Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
title_sort |
detecting causality by combined use of multiple methods: climate and brain examples |
url |
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158572 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158572&type=printable |
genre |
ice core |
genre_facet |
ice core |
op_relation |
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0158572 https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158572&type=printable |
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1766029571401973760 |