ArticleNature communications2020
Partial cross mapping eliminates indirect causal influences.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Environmental drivers of influenza A and B: causal analysis and forecasting.BMC public health · 2026Article
- HSTXGB: a hyperparameter self-tuning XGBoost method integrating pre- and post-processing for gene regulatory network inference.Frontiers in genetics · 2026Article
- Detecting dynamical causality by intersection cardinal concavity.Fundamental research · 2025Article
- Reconstructing gene network structure and dynamics from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Quantifying interventional causality by knockoff operation.Science advances · 2025Article
- Individualized Frequency-Dependent Stimulation to the Anterior Nucleus of the Thalamus May Improve Antiepileptic Effects in Patients With Focal Epilepsy.CNS neuroscience & therapeutics · 2025Article
- Detecting existence of a hidden mediator between a pair of individual time series.Scientific reports · 2025Article
- Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.Nature communications · 2025Article
- Assessing the influencing factors of dengue fever in Chinese mainland based on causal analysis.Scientific reports · 2025Article
- Exploring Causal Network Complexity in Industrial Linkages: A Comparative Study.Entropy (Basel, Switzerland) · 2025Article
- Decomposing causality into its synergistic, unique, and redundant components.Nature communications · 2024Article
- On formal limitations of causal ecological networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture.National science review · 2024Article
- Higher-order Granger reservoir computing: simultaneously achieving scalable complex structures inference and accurate dynamics prediction.Nature communications · 2024Article
- Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach.Heliyon · 2024Article
- Associating frailty and dynamic dysregulation between motor and cardiac autonomic systems.Frontiers in aging · 2024Article
- A general model-based causal inference method overcomes the curse of synchrony and indirect effect.Nature communications · 2023Article
- Modern causal inference approaches to investigate biodiversity-ecosystem functioning relationships.Nature communications · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Causality detection likely misidentifies indirect causations as direct ones, due to the effect of causation transitivity. Although several methods in traditional frameworks have been proposed to avoid such misinterpretations, there still is a lack of feasible methods for identifying direct causations from indirect ones in the challenging situation where the variables of the underlying dynamical system are non-separable and weakly or moderately interacting. Here, we solve this problem by developing a data-based, model-independent method of partial cross mapping based on an articulated integration of three tools from nonlinear dynamics and statistics: phase-space reconstruction, mutual cross mapping, and partial correlation. We demonstrate our method by using data from different representative models and real-world systems. As direct causations are keys to the fundamental underpinnings of a variety of complex dynamics, we anticipate our method to be indispensable in unlocking and deciphering the inner mechanisms of real systems in diverse disciplines from data.
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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.