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Selecting robust features for machine-learning applications using multidata causal discovery

Sudheesh, Saranya Ganesh and Beucler, Tom and Tam, Frederick Iat-Hin and Gomez, Milton S. and Runge, Jakob and Gerhardus, Andreas (2023) Selecting robust features for machine-learning applications using multidata causal discovery. Environmental data science, 2. Cambridge University Press. doi: 10.1017/eds.2023.21. ISSN 2634-4602.

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Official URL: https://www.cambridge.org/core/journals/environmental-data-science/article/selecting-robust-features-for-machinelearning-applications-using-multidata-causal-discovery/29C08A0FF7BFD2347768F315E041A143


Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally spurious links before passing the remaining causal features as inputs to ML models (multiple linear regression and random forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific tropical cyclones (TCs), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, noncausal ML, and other feature selection methods (lagged correlation and random), even slightly outperforming feature selection based on explainable artificial intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.

Item URL in elib:https://elib.dlr.de/196209/
Document Type:Article
Title:Selecting robust features for machine-learning applications using multidata causal discovery
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sudheesh, Saranya GaneshUniversity of LausanneUNSPECIFIEDUNSPECIFIED
Beucler, TomUniversity of Lausanne, Lausanne, Switzerlandhttps://orcid.org/0000-0002-5731-1040UNSPECIFIED
Tam, Frederick Iat-HinUniversity of LausanneUNSPECIFIEDUNSPECIFIED
Gomez, Milton S.University of LausanneUNSPECIFIEDUNSPECIFIED
Date:July 2023
Journal or Publication Title:Environmental data science
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Publisher:Cambridge University Press
Keywords:causal feature selection; machine learning; multivariate time series analysis; tropical cyclones
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 > Data Analysis and Intelligence
Deposited By: Gerhardus, Andreas
Deposited On:26 Jul 2023 11:51
Last Modified:01 Aug 2023 08:53

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