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Cited by 2,539

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Detecting Causality in Complex Ecosystems

Sugihara, George, May, Robert, Ye, Hao, Hsieh, Chih-hao, Deyle, Ethan, Fogarty, Michael, Munch, Stephan·2012·Science

Abstract

Cause or Correlation?: Three centuries ago, Bishop Berkeley's 1710 classic “A treatise on the nature of human knowledge,” first spelled out the “correlation vs. causation” dilemma. Sugihara et al. (p. 496 , published online 20 September) present an approach to this conundrum, and extend current discussions about causation to dynamic systems with weak to moderate coupling (such as ecosystems). The resulting method, convergent cross mapping can detect causal linkages between time series.

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Metadata

Authors
Sugihara, G., May, R., Ye, H., Hsieh, C.-H., Deyle, E., Fogarty, M., & Munch, S.
Venue
Science
Published
2012-10-26
Volume
338
Issue
6106
Pages
496-500
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
OpenAlex
W2083278075
Cited by
2,539
References
7
FWCI
49.28
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Detecting Causality in Complex Ecosystems. https://app.cochat.ai/papers/10.1126/science.1227079
The paper
Sugihara, G., May, R., Ye, H., Hsieh, C.-H., Deyle, E., Fogarty, M., & Munch, S. (2012). Detecting Causality in Complex Ecosystems. Science. https://doi.org/10.1126/science.1227079

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.