ArticleNature communications2018
Causal decomposition in the mutual causation system.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Quantifying interventional causality by knockoff operation.Science advances · 2025Article
- The impact of data resolution on dynamic causal inference in multiscale ecological networks.Communications biology · 2024Article
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- A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia.Frontiers in neuroscience · 2023Article
- Neighbours and relatives: accounting for spatial distribution when testing causal hypotheses in cultural evolution.Evolutionary human sciences · 2023Article
- Multi-scale causality analysis between COVID-19 cases and mobility level using ensemble empirical mode decomposition and causal decomposition.Physica A · 2022Article
- Reply To: Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition.Nature communications · 2022Article
- Comments on identifying causal relationships in nonlinear dynamical systems via empirical mode decomposition.Nature communications · 2022Article
- Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.Frontiers in neuroinformatics · 2022Article
- On the impact of publicly available news and information transfer to financial markets.Royal Society open science · 2021Article
- New insights and best practices for the successful use of Empirical Mode Decomposition, Iterative Filtering and derived algorithms.Scientific reports · 2020Article
- Waveform changes with the evolution of beta bursts in the human subthalamic nucleus.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2020Article
- Causality Detection Methods Applied to the Investigation of Malaria Epidemics.Entropy (Basel, Switzerland) · 2019Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator-prey systems.
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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.