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Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships

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  • Jakob Runge

    (Institut für Datenwissenschaften
    Institute of Computer Engineering and Microelectronics)

Abstract

Detecting and quantifying the causal relations of ecosystem functioning is a challenging endeavor. A global study on grasslands illustrates how reasoning about underlying assumptions, from confounding and nonlinearity to fundamental questions of determinism, is key to unlocking the potential of modern causal inference approaches in ecology.

Suggested Citation

  • Jakob Runge, 2023. "Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships," Nature Communications, Nature, vol. 14(1), pages 1-3, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37546-1
    DOI: 10.1038/s41467-023-37546-1
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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. James B. Grace & T. Michael Anderson & Eric W. Seabloom & Elizabeth T. Borer & Peter B. Adler & W. Stanley Harpole & Yann Hautier & Helmut Hillebrand & Eric M. Lind & Meelis Pärtel & Jonathan D. Bakke, 2016. "Integrative modelling reveals mechanisms linking productivity and plant species richness," Nature, Nature, vol. 529(7586), pages 390-393, January.
    4. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    5. Siyang Leng & Huanfei Ma & Jürgen Kurths & Ying-Cheng Lai & Wei Lin & Kazuyuki Aihara & Luonan Chen, 2020. "Partial cross mapping eliminates indirect causal influences," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881.
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