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Causal modelling of heavy-tailed variables and confounders with application to river flow
Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423152/ https://www.ncbi.nlm.nih.gov/pubmed/37581203 http://dx.doi.org/10.1007/s10687-022-00456-4 |
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author | Pasche, Olivier C. Chavez-Demoulin, Valérie Davison, Anthony C. |
author_facet | Pasche, Olivier C. Chavez-Demoulin, Valérie Davison, Anthony C. |
author_sort | Pasche, Olivier C. |
collection | PubMed |
description | Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the effect of a known potential confounder to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We also introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10687-022-00456-4. |
format | Online Article Text |
id | pubmed-10423152 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-104231522023-08-14 Causal modelling of heavy-tailed variables and confounders with application to river flow Pasche, Olivier C. Chavez-Demoulin, Valérie Davison, Anthony C. Extremes (Boston) Article Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows and precipitation, we introduce a new causal discovery methodology for heavy-tailed variables that allows the effect of a known potential confounder to be almost entirely removed when the variables have comparable tails, and also decreases it sufficiently to enable correct causal inference when the confounder has a heavier tail. We also introduce a new parametric estimator for the existing causal tail coefficient and a permutation test. Simulations show that the methods work well and the ideas are applied to the motivating dataset. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10687-022-00456-4. Springer US 2022-12-17 2023 /pmc/articles/PMC10423152/ /pubmed/37581203 http://dx.doi.org/10.1007/s10687-022-00456-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Pasche, Olivier C. Chavez-Demoulin, Valérie Davison, Anthony C. Causal modelling of heavy-tailed variables and confounders with application to river flow |
title | Causal modelling of heavy-tailed variables and confounders with application to river flow |
title_full | Causal modelling of heavy-tailed variables and confounders with application to river flow |
title_fullStr | Causal modelling of heavy-tailed variables and confounders with application to river flow |
title_full_unstemmed | Causal modelling of heavy-tailed variables and confounders with application to river flow |
title_short | Causal modelling of heavy-tailed variables and confounders with application to river flow |
title_sort | causal modelling of heavy-tailed variables and confounders with application to river flow |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10423152/ https://www.ncbi.nlm.nih.gov/pubmed/37581203 http://dx.doi.org/10.1007/s10687-022-00456-4 |
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