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Convergent Cross Mapping/Evidence
Method evidence record

Convergent Cross Mapping

Convergent Cross Mapping (CCM) is a nonlinear, state-space method for detecting causality between time-series variables embedded in a shared dynamical system. Introduced by George Sugihara and colleagues in their landmark 2012 Science paper, CCM exploits Takens' embedding theorem: if variable X causally influences Y, the historical record of Y contains enough information to recover the states of X. Causality is confirmed when cross-map skill improves—converges—as the time-series library grows longer.

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Source record

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Convergent Cross Mapping (CCM)
Taxonomic method record · ml-model / causal-inference
  • Sugihara, G., et al. (2012). Detecting causality in complex ecosystems. Science, 338(6106), 496–500. · DOI 10.1126/science.1227079
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

See alsoGranger Causalitymachine-suggested · Relational suggestion, not evidence.Same method familyRecurrence Quantification Analysismachine-suggested · Relational suggestion, not evidence.Same method familyTransfer Entropymachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

1 recorded citation, copied from the method source record.

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