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Data-driven causal analysis of observational biological time series
Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often contr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391047/ https://www.ncbi.nlm.nih.gov/pubmed/35983746 http://dx.doi.org/10.7554/eLife.72518 |
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author | Yuan, Alex Eric Shou, Wenying |
author_facet | Yuan, Alex Eric Shou, Wenying |
author_sort | Yuan, Alex Eric |
collection | PubMed |
description | Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8). |
format | Online Article Text |
id | pubmed-9391047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-93910472022-08-20 Data-driven causal analysis of observational biological time series Yuan, Alex Eric Shou, Wenying eLife Computational and Systems Biology Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8). eLife Sciences Publications, Ltd 2022-08-19 /pmc/articles/PMC9391047/ /pubmed/35983746 http://dx.doi.org/10.7554/eLife.72518 Text en © 2022, Yuan and Shou https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Yuan, Alex Eric Shou, Wenying Data-driven causal analysis of observational biological time series |
title | Data-driven causal analysis of observational biological time series |
title_full | Data-driven causal analysis of observational biological time series |
title_fullStr | Data-driven causal analysis of observational biological time series |
title_full_unstemmed | Data-driven causal analysis of observational biological time series |
title_short | Data-driven causal analysis of observational biological time series |
title_sort | data-driven causal analysis of observational biological time series |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391047/ https://www.ncbi.nlm.nih.gov/pubmed/35983746 http://dx.doi.org/10.7554/eLife.72518 |
work_keys_str_mv | AT yuanalexeric datadrivencausalanalysisofobservationalbiologicaltimeseries AT shouwenying datadrivencausalanalysisofobservationalbiologicaltimeseries |