ArticleScience advances2019
Detecting and quantifying causal associations in large nonlinear time series datasets.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 134 papers, 1 of them a synthesis that pooled it.
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Who cites it
134 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards IoT-Fog-ML integration for temperature break detection and prediction in fresh produce cold chains: a systematic review and architectural framework.Frontiers in artificial intelligence · 2026Pooled it
- Deciphering interventional dynamical causality from non-intervention complex systems.Innovation (Cambridge (Mass.)) · 2026Article
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Towards precision medicine: Application of the structural causal model in psychiatry.General psychiatry · 2026Article
- TFPAG-Net: A Time-Frequency Dual-Branch Fusion and PCMCI-Based Association-Guided Network for IIoT Intrusion Detection.Sensors (Basel, Switzerland) · 2026Article
- lagCI enables inference of temporal causal relationships from dense multi-omic time series.Briefings in bioinformatics · 2026Article
- Multi-Horizon Herd-Based Cattle Live-Weight Forecasting Using Irregular Automated Weighing Data.Animals : an open access journal from MDPI · 2026Article
- Greenhouse warming exacerbates El Niño-induced Indian monsoon droughts.Nature communications · 2026Article
- Time Series Correlations and Kolmogorov Complexity: A Hausdorff Dimension Perspective.Entropy (Basel, Switzerland) · 2026Article
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- A causal discovery framework for digital phenotyping.Scientific reports · 2026Article
- Identification of GPX3 as a key biomarker of placental ferroptosis in gestational diabetes mellitus via bioinformatics and clinical analysis.Molecular diversity · 2026Article
- Exposing the potential of XAI-based causal discovery for analysing unstable rock slopes.Scientific reports · 2026Article
- Contribution of North Atlantic temperature variability to El Niño cyclicity as revealed by spectral causality estimates.Scientific reports · 2026Article
- Disease burden associated with influenza activity at the population level.Epidemiology and infection · 2026Article
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- Best practices for moving from correlation to causation in ecological research.Nature communications · 2026Review
- Assimilative causal inference.Nature communications · 2026Article
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74 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
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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.