ArticleScientific reports2014
Detecting causality from nonlinear dynamics with short-term time series.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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.
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.
Who cites it
26 citing papers in PubMed.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Detecting dynamical causality by intersection cardinal concavity.Fundamental research · 2025Article
- Longitudinal Associations between Weight Self-Stigma, Physical Exercise, and Social Anxiety among Adolescents: Between-and Within-Person Effects.The Psychiatric quarterly · 2025Article
- Inferring directed spectral information flow between mixed-frequency time series.Research square · 2025Article
- Friction and Stiffness Dependent Dynamics of Accumulation Landslides with Delayed Failure.Entropy (Basel, Switzerland) · 2023Article
- Article
- The geometry of synchronization: quantifying the coupling direction of physiological signals of stress between individuals using inter-system recurrence networks.Frontiers in network physiology · 2023Article
- Review
- Continuity Scaling: A Rigorous Framework for Detecting and Quantifying Causality Accurately.Research (Washington, D.C.) · 2022Article
- Convergent cross sorting for estimating dynamic coupling.Scientific reports · 2021Article
- Fast and effective pseudo transfer entropy for bivariate data-driven causal inference.Scientific reports · 2021Article
- Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data.Scientific reports · 2021Article
- Article
- Partial cross mapping eliminates indirect causal influences.Nature communications · 2020Article
- DISCOVERING CAUSALITIES FROM CARDIOTOCOGRAPHY SIGNALS USING IMPROVED CONVERGENT CROSS MAPPING WITH GAUSSIAN PROCESSES.Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference) · 2020Article
- The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.Frontiers in physiology · 2020Article
- Detecting Causality using Deep Gaussian Processes.Conference record. Asilomar Conference on Signals, Systems & Computers · 2019Article
- Bypassing the volume conduction effect by multilayer neural network for effective connectivity estimation.Medical & biological engineering & computing · 2019Article
- Field transcriptome revealed a novel relationship between nitrate transport and flowering in Japanese beech.Scientific reports · 2019Article
- Dynamical differential expression (DyDE) reveals the period control mechanisms of the Arabidopsis circadian oscillator.PLoS computational biology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantifying causality between variables from observed time series data is of great importance in various disciplines but also a challenging task, especially when the observed data are short. Unlike the conventional methods, we find it possible to detect causality only with very short time series data, based on embedding theory of an attractor for nonlinear dynamics. Specifically, we first show that measuring the smoothness of a cross map between two observed variables can be used to detect a causal relation. Then, we provide a very effective algorithm to computationally evaluate the smoothness of the cross map, or "Cross Map Smoothness" (CMS), and thus to infer the causality, which can achieve high accuracy even with very short time series data. Analysis of both mathematical models from various benchmarks and real data from biological systems validates our method.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.