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Causal Discovery for Climate Time Series in the Presence of Unobserved Variables

Gerhardus, Andreas and Runge, Jakob (2020) Causal Discovery for Climate Time Series in the Presence of Unobserved Variables. EGU General Assembly 2020, 04.-08. Mai 2020, Online. doi: 10.5194/egusphere-egu2020-9270.

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Scientific inquiry seeks to understand natural phenomena by understanding their underlying processes, i.e., by identifying cause and effect. In addition to mere scientific curiosity, an understanding of cause and effect relationships is necessary to predict the effect of changing dynamical regimes and for the attribution of extreme events to potential causes. It is thus an important question to ask how, in cases where controlled experiments are not feasible, causation can still be inferred from the statistical dependencies in observed time series. A central obstacle for such an inference is the potential existence of unobserved causally relevant variables. Arguably, this is more likely to be the case than not, for example unmeasured deep oceanic variables in atmospheric processes. Unobserved variables can act as confounders (meaning they are a common cause of two or more observed variables) and thus introduce spurious, i.e., non-causal dependencies. Despite these complications, the last three decades have seen the development of so-called causal discovery algorithms (an example being FCI by Spirtes et al., 1999) that are often able to identify spurious associations and to distinguish them from genuine causation. This opens the possibility for a data-driven approach to infer cause and effect relationships among climate variables, thereby contributing to a better understanding of Earth's complex climate system. These methods are, however, not yet well adapted to some specific challenges that climate time series often come with, e.g. strong autocorrelation, time lags and nonlinearities. To close this methodological gap, we generalize the ideas of the recent PCMCI causal discovery algorithm (Runge et al., 2019) to time series where unobserved causally relevant variables may exist (in contrast, PCMCI made the assumption of no confounding). Further, we present preliminary applications to modes of climate variability.

Item URL in elib:https://elib.dlr.de/135274/
Document Type:Conference or Workshop Item (Speech)
Title:Causal Discovery for Climate Time Series in the Presence of Unobserved Variables
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Gerhardus, AndreasUNSPECIFIEDhttps://orcid.org/0000-0003-1868-655XUNSPECIFIED
Runge, JakobUNSPECIFIEDhttps://orcid.org/0000-0002-0629-1772UNSPECIFIED
Date:May 2020
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:causal discovery; time series analysis; causal inference; causality; climate; hidden variables
Event Title:EGU General Assembly 2020
Event Location:Online
Event Type:international Conference
Event Dates:04.-08. Mai 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:other
DLR - Research area:Raumfahrt
DLR - Program:R - no assignment
DLR - Research theme (Project):R - no assignment
Location: Jena
Institutes and Institutions:Institute of Data Science > Datamangagement and Analysis
Deposited By: Gerhardus, Andreas
Deposited On:03 Dec 2020 14:25
Last Modified:03 Dec 2020 14:25

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