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...
Published in: | PLOS ONE |
---|---|
Main Authors: | , , , , , |
Format: | Text |
Language: | English |
Published: |
Public Library of Science
2016
|
Subjects: | |
Online Access: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933387/ http://www.ncbi.nlm.nih.gov/pubmed/27380515 https://doi.org/10.1371/journal.pone.0158572 |
id |
ftpubmed:oai:pubmedcentral.nih.gov:4933387 |
---|---|
record_format |
openpolar |
spelling |
ftpubmed:oai:pubmedcentral.nih.gov:4933387 2023-05-15T16:39:15+02:00 Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples Hirata, Yoshito Amigó, José M. Matsuzaka, Yoshiya Yokota, Ryo Mushiake, Hajime Aihara, Kazuyuki 2016-07-05 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933387/ http://www.ncbi.nlm.nih.gov/pubmed/27380515 https://doi.org/10.1371/journal.pone.0158572 en eng Public Library of Science http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933387/ http://www.ncbi.nlm.nih.gov/pubmed/27380515 http://dx.doi.org/10.1371/journal.pone.0158572 © 2016 Hirata et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. CC-BY Research Article Text 2016 ftpubmed https://doi.org/10.1371/journal.pone.0158572 2016-07-24T00:08:04Z 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. Text ice core PubMed Central (PMC) PLOS ONE 11 7 e0158572 |
institution |
Open Polar |
collection |
PubMed Central (PMC) |
op_collection_id |
ftpubmed |
language |
English |
topic |
Research Article |
spellingShingle |
Research Article Hirata, Yoshito Amigó, José M. Matsuzaka, Yoshiya Yokota, Ryo Mushiake, Hajime Aihara, Kazuyuki Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples |
topic_facet |
Research Article |
description |
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 |
Text |
author |
Hirata, Yoshito Amigó, José M. Matsuzaka, Yoshiya Yokota, Ryo Mushiake, Hajime Aihara, Kazuyuki |
author_facet |
Hirata, Yoshito Amigó, José M. Matsuzaka, Yoshiya Yokota, Ryo Mushiake, Hajime Aihara, Kazuyuki |
author_sort |
Hirata, Yoshito |
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 |
publisher |
Public Library of Science |
publishDate |
2016 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933387/ http://www.ncbi.nlm.nih.gov/pubmed/27380515 https://doi.org/10.1371/journal.pone.0158572 |
genre |
ice core |
genre_facet |
ice core |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4933387/ http://www.ncbi.nlm.nih.gov/pubmed/27380515 http://dx.doi.org/10.1371/journal.pone.0158572 |
op_rights |
© 2016 Hirata et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.1371/journal.pone.0158572 |
container_title |
PLOS ONE |
container_volume |
11 |
container_issue |
7 |
container_start_page |
e0158572 |
_version_ |
1766029586110349312 |