ArticleIEEE transactions on pattern analysis and machine intelligence2022
Neural Granger Causality.
Article in IEEE transactions on pattern analysis and machine intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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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.
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Who cites it
37 citing papers in PubMed.
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Interpretable Event-Driven Multisensor Risk-Evolution Analysis for Methane Early Warning.Sensors (Basel, Switzerland) · 2026Article
- Mapping Whole-Brain Nonlinear Structure-Function Dynamics in Aging via Neural Granger Causality.Brain topography · 2026Article
- Decoding the causal drivers of spatial cellular topology.iScience · 2026Article
- A causal discovery framework for digital phenotyping.Scientific reports · 2026Article
- The East Asian climate-warfare nexus underwent a paradigm shift across pre-industrial and industrial eras.Scientific reports · 2026Article
- A Deep Spatio-Temporal Architecture for Dynamic ECN Analysis with Granger Causality based Causal Discovery.Pattern recognition · 2026Article
- Multivariate Timing and Granger Causality Analysis of Spontaneous Facial Mimicry in Response to Android Dynamic Facial Expressions.Sensors (Basel, Switzerland) · 2026Article
- On the virtues and limitations of Granger-causal brain connectivity estimate: Critical analysis using neural mass models.Network neuroscience (Cambridge, Mass.) · 2026Article
- Association between weaning stress and rumen microbiota in goat kids: evidence from granger causality and randomized controlled trial validation.Animal bioscience · 2026Article
- Jacobian Granger causality for count and binary data with applications to causal network inference.Scientific reports · 2025Article
- Abnormal Enhanced Gamma Synchronization in the Default Mode Network Associated With Chronic Pain.European journal of neurology · 2025Article
- Methods for Brain Connectivity Analysis with Applications to Rat Local Field Potential Recordings.Entropy (Basel, Switzerland) · 2025Article
- Artificial Intelligence (AI)-driven approach to climate action and sustainable development.Nature communications · 2025Article
- Structural assessment of family and educational influences on student health behaviours: Insights from a public health perspective.PloS one · 2025Article
- A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.IEEE transactions on bio-medical engineering · 2024Article
- Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors.Cell systems · 2024Article
- Collective relational inference for learning heterogeneous interactions.Nature communications · 2024Article
- Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation.Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management · 2024Article
- Causal Inference Meets Deep Learning: A Comprehensive Survey.Research (Washington, D.C.) · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero-in particular, through the use of convex group-lasso penalties-we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset.
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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.