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Cited by 979Bayesian Modeling and Causal InferenceStatistical Methods and InferencePhilosophy and History of Science

Detecting and quantifying causal associations in large nonlinear time series datasets

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Runge, Jakob, Nowack, Peer, Kretschmer, Marlene, Flaxman, Seth, Sejdinovic, Dino·2019·Science Advances
DOI 10.1126/sciadv.aau4996PMC PMC6881151
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Abstract

A novel causal discovery method for estimating nonlinear interdependency networks from large time series datasets.

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  • Subsequent improvements such as RFCI, PC-Stable, and PCMCI have consistently failed to overcome the fundamental flaw that CI tests are highly prone to statistical errors under high-dimensional, sparse logs.

    “Although subsequent improvements exist, such as RFCI[14] (sacrificing information for speed), PC-Stable[15] (eliminating order dependency), and PCMCI[16] for time-series data, they have consistently failed to overcome the fundamental flaw that CI tests are highly prone to statistical errors under high-dimensional, sparse logs.”

    From From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps (2026) · cites this paper as [16]

    The paper does identify PCMCI as a method for time-series data, but it contradicts the claim that PCMCI consistently fails to address CI-test errors in high-dimensional, sparse data. The Theoretical properties of PCMCI section says the method exploits network sparsity and controls false positives; the Discussion and conclusion reports higher detection power for datasets with dozens to hundreds of variables and correctly controlled false-positive rates. (LLM, conf=0.88)

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Authors
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D.
Affiliations
  • Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
  • Imperial College London (GB)
  • Potsdam Institute for Climate Impact Research (DE)
  • The Alan Turing Institute for Data Science, London NW1 3DB, UK.
Corresponding author
Runge, Jakob
ORCID
Venue
Science Advances
Publisher
American Association for the Advancement of Science (AAAS)
Published
2019-11
Volume
5
Issue
11
Pages
eaau4996-eaau4996
ISSN
2375-2548 (ISSN-L)
DOI
10.1126/sciadv.aau4996
PMC
PMC6881151
OpenAlex
W2593118993
Cited by
979
References
10
Citations by year
2026: 258 · 2025: 219 · 2024: 169 · 2023: 113 · 2022: 106 · 2021: 72 · 2020: 33 · 2019: 5 · 2018: 4
FWCI
24.63
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Science Advances (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • CentAUR (University of Reading) (acceptedVersion)
  • UEA Digital Repository (University of East Anglia) (acceptedVersion)
  • elib (German Aerospace Center) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Spiral (Imperial College London) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • James S. McDonnell Foundation
  • European Regional Development Fund
  • EPSRC (EP/N510129/1)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy and History of Science
Keywords
causal discovery, causal networks, nonlinear time series, conditional independence testing, causal inference, high-dimensional data, climate dynamics, heart dynamics, synthetic time series, detection power

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