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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4933387?pdf=render |
| _version_ | 1819260364557123584 |
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| author | Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara |
| author_facet | Yoshito Hirata José M Amigó Yoshiya Matsuzaka Ryo Yokota Hajime Mushiake Kazuyuki Aihara |
| author_sort | Yoshito Hirata |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-23T19:24:44Z |
| format | Article |
| id | doaj.art-378b7eeddf7f422d830158fad0c4261c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-23T19:24:44Z |
| publishDate | 2016-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-378b7eeddf7f422d830158fad0c4261c2022-12-21T17:34:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01117e015857210.1371/journal.pone.0158572Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples.Yoshito HirataJosé M AmigóYoshiya MatsuzakaRyo YokotaHajime MushiakeKazuyuki AiharaIdentifying 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.http://europepmc.org/articles/PMC4933387?pdf=render |
| 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. PLoS ONE |
| title | 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_short | 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 | http://europepmc.org/articles/PMC4933387?pdf=render |
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