The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Inferring causation from time series in Earth system sciences
Open accessAbstract
The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
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Citations
Cites
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
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Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Estimating causal effects of treatments in randomized and nonrandomized studies
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Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (2001) · doi:10.7551/mitpress/1754.001.0001 · Cited by 4,521
Self-citationCausal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Imbens, G. W., & Rubin, D. B. (2015) · doi:10.1017/cbo9781139025751 · Cited by 4,210
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Probabilistic machine learning and artificial intelligence
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Detecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979
Self-citationCausal network reconstruction from time series: From theoretical assumptions to practical estimation
Runge, J. (2018) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5025050 · Cited by 422
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Cited by
Detecting and quantifying causal associations in large nonlinear time series datasets
Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019) · Science Advances · doi:10.1126/sciadv.aau4996 · Cited by 979
Self-citationHeat Waves: Physical Understanding and Scientific Challenges
Barriopedro, D., García‐Herrera, R., Ordóñez, C., Miralles, D. G., & Salcedo‐Sanz, S. (2023) · Reviews of Geophysics · doi:10.1029/2022rg000780 · Cited by 404
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Irrgang, C., Boers, N., Sonnewald, M., Barnes, E. A., Kadow, C., Staneva, J., & Saynisch-Wagner, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00374-3 · Cited by 308
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
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A review of long-term pesticide monitoring studies to assess surface water quality trends
Chow, R., Scheidegger, R., Doppler, T., Dietzel, A., Fenicia, F., & Stamm, C. (2020) · Water Research X · doi:10.1016/j.wroa.2020.100064 · Cited by 156
Metadata
- Authors
- Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., & Zscheischler, J.
- Venue
- Nature Communications
- Published
- 2019-06-14
- Volume
- 10
- Issue
- 1
- Pages
- 2553-2553
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2949505632
- Cited by
- 984
- References
- 12
- FWCI
- 38.91
- Type
- journal article
- Language
- en
- Open access
- gold
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- The paper
- Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., . . . Zscheischler, J. (2019). Inferring causation from time series in Earth system sciences. Nature Communications. https://doi.org/10.1038/s41467-019-10105-3
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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