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Causal inference for time series

Runge, Jakob and Gerhardus, Andreas and Varando, Gherardo and Eyring, Veronika and Camps-Valls, Gustau (2023) Causal inference for time series. Nature Reviews Earth and Environment, 4 (7), pp. 487-505. Springer Nature. doi: 10.1038/s43017-023-00431-y. ISSN 2662-138X.

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Official URL: https://dx.doi.org/10.1038/s43017-023-00431-y


Many research questions in Earth and environmental sciences are inherently causal, requiring robust analyses to establish whether and how changes in one variable cause changes in another. Causal inference provides the theoretical foundations to use data and qualitative domain knowledge to quantitatively answer these questions, complementing statistics and machine learning techniques. However, there is still a broad language gap between the methodological and domain science communities. In this Technical Review, we explain the use of causal inference frameworks with a focus on the challenges of time series data. Domain-adapted explanations, method guidance and practical case studies provide an accessible summary of methods for causal discovery and causal efect estimation. Examples from climate and biogeosciences illustrate typical challenges, such as contemporaneous causation, hidden confounding and non-stationarity, and some strategies to address these challenges. Integrating causal thinking into data-driven science will facilitate process understanding and more robust machine learning and statistical models for Earth and environmental sciences, enabling the tackling of many open problems with relevant environmental, economic and societal implications.

Item URL in elib:https://elib.dlr.de/195986/
Document Type:Article
Title:Causal inference for time series
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Runge, JakobInstitute of Data Sciencehttps://orcid.org/0000-0002-0629-1772UNSPECIFIED
Gerhardus, AndreasInstitute of Data Sciencehttps://orcid.org/0000-0003-1868-655XUNSPECIFIED
Varando, GherardoTU Berlinhttps://orcid.org/0000-0002-6708-1103UNSPECIFIED
Eyring, VeronikaDLR, IPAhttps://orcid.org/0000-0002-6887-4885UNSPECIFIED
Camps-Valls, GustauUniversity of Valencia, Valencia, Spainhttps://orcid.org/0000-0003-1683-2138UNSPECIFIED
Date:27 June 2023
Journal or Publication Title:Nature Reviews Earth and Environment
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 487-505
Publisher:Springer Nature
Keywords:causal inference, time series
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Earth System Model Evaluation and Analysis
Deposited By: Stockinger, Pascal
Deposited On:13 Jul 2023 16:07
Last Modified:19 Oct 2023 15:26

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