Evidence map›Paper›PMID 26435402›Full record

ArticleScientific reports2015

Distinguishing time-delayed causal interactions using convergent cross mapping.

Hao Ye, Ethan R Deyle, Luis J Gilarranz, George Sugihara

Abstract read
In one paragraph

Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 109 papers.

0numbers the graph read from it
0cells of the map it votes in
109citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

109 citing papers in PubMed.

  1. Article
  2. Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026
    Review
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  12. Assimilative causal inference.Nature communications · 2026
    Article
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  18. Review
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49 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Hao YeScripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
Ethan R DeyleScripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.
Luis J GilarranzIntegrative Ecology Group, Estación Biológica de Doñana, CSIC, Sevilla, Spain.
George SugiharaScripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An important problem across many scientific fields is the identification of causal effects from observational data alone. Recent methods (convergent cross mapping, CCM) have made substantial progress on this problem by applying the idea of nonlinear attractor reconstruction to time series data. Here, we expand upon the technique of CCM by explicitly considering time lags. Applying this extended method to representative examples (model simulations, a laboratory predator-prey experiment, temperature and greenhouse gas reconstructions from the Vostok ice core, and long-term ecological time series collected in the Southern California Bight), we demonstrate the ability to identify different time-delayed interactions, distinguish between synchrony induced by strong unidirectional-forcing and true bidirectional causality, and resolve transitive causal chains.

Identifiers

PMID26435402
PMCPMC4592974

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.