A comparison of two causal methods in the context of climate analyses
Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary condi...
Published in: | Nonlinear Processes in Geophysics |
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00071966 2024-04-14T08:08:06+00:00 A comparison of two causal methods in the context of climate analyses Docquier, David Di Capua, Giorgia Donner, Reik V. Pires, Carlos A. L. Simon, Amélie Vannitsem, Stéphane 2024-02 electronic https://doi.org/10.5194/npg-31-115-2024 https://noa.gwlb.de/receive/cop_mods_00071966 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070203/npg-31-115-2024.pdf https://npg.copernicus.org/articles/31/115/2024/npg-31-115-2024.pdf eng eng Copernicus Publications Nonlinear Processes in Geophysics -- http://www.bibliothek.uni-regensburg.de/ezeit/?2078085 -- http://www.nonlin-processes-geophys.net/ -- http://www.copernicus.org/EGU/npg/npg.htm -- 1607-7946 https://doi.org/10.5194/npg-31-115-2024 https://noa.gwlb.de/receive/cop_mods_00071966 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070203/npg-31-115-2024.pdf https://npg.copernicus.org/articles/31/115/2024/npg-31-115-2024.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2024 ftnonlinearchiv https://doi.org/10.5194/npg-31-115-2024 2024-03-19T12:18:16Z Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and we apply them to four different artificial models of increasing complexity and one real-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables and with PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study with climate indices, both methods present some similarities and differences at monthly timescale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods. Article in Journal/Newspaper Arctic Niedersächsisches Online-Archiv NOA Arctic Pacific Nonlinear Processes in Geophysics 31 1 115 136 |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Docquier, David Di Capua, Giorgia Donner, Reik V. Pires, Carlos A. L. Simon, Amélie Vannitsem, Stéphane A comparison of two causal methods in the context of climate analyses |
topic_facet |
article Verlagsveröffentlichung |
description |
Correlation does not necessarily imply causation, and this is why causal methods have been developed to try to disentangle true causal links from spurious relationships. In our study, we use two causal methods, namely, the Liang–Kleeman information flow (LKIF) and the Peter and Clark momentary conditional independence (PCMCI) algorithm, and we apply them to four different artificial models of increasing complexity and one real-world case study based on climate indices in the Atlantic and Pacific regions. We show that both methods are superior to the classical correlation analysis, especially in removing spurious links. LKIF and PCMCI display some strengths and weaknesses for the three simplest models, with LKIF performing better with a smaller number of variables and with PCMCI being best with a larger number of variables. Detecting causal links from the fourth model is more challenging as the system is nonlinear and chaotic. For the real-world case study with climate indices, both methods present some similarities and differences at monthly timescale. One of the key differences is that LKIF identifies the Arctic Oscillation (AO) as the largest driver, while the El Niño–Southern Oscillation (ENSO) is the main influencing variable for PCMCI. More research is needed to confirm these links, in particular including nonlinear causal methods. |
format |
Article in Journal/Newspaper |
author |
Docquier, David Di Capua, Giorgia Donner, Reik V. Pires, Carlos A. L. Simon, Amélie Vannitsem, Stéphane |
author_facet |
Docquier, David Di Capua, Giorgia Donner, Reik V. Pires, Carlos A. L. Simon, Amélie Vannitsem, Stéphane |
author_sort |
Docquier, David |
title |
A comparison of two causal methods in the context of climate analyses |
title_short |
A comparison of two causal methods in the context of climate analyses |
title_full |
A comparison of two causal methods in the context of climate analyses |
title_fullStr |
A comparison of two causal methods in the context of climate analyses |
title_full_unstemmed |
A comparison of two causal methods in the context of climate analyses |
title_sort |
comparison of two causal methods in the context of climate analyses |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/npg-31-115-2024 https://noa.gwlb.de/receive/cop_mods_00071966 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070203/npg-31-115-2024.pdf https://npg.copernicus.org/articles/31/115/2024/npg-31-115-2024.pdf |
geographic |
Arctic Pacific |
geographic_facet |
Arctic Pacific |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
Nonlinear Processes in Geophysics -- http://www.bibliothek.uni-regensburg.de/ezeit/?2078085 -- http://www.nonlin-processes-geophys.net/ -- http://www.copernicus.org/EGU/npg/npg.htm -- 1607-7946 https://doi.org/10.5194/npg-31-115-2024 https://noa.gwlb.de/receive/cop_mods_00071966 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00070203/npg-31-115-2024.pdf https://npg.copernicus.org/articles/31/115/2024/npg-31-115-2024.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.5194/npg-31-115-2024 |
container_title |
Nonlinear Processes in Geophysics |
container_volume |
31 |
container_issue |
1 |
container_start_page |
115 |
op_container_end_page |
136 |
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