mlsanityData to 7 Oct 2026

Granger Causality: A Review and Recent Advances

arXiv 2105.02675 ↗First posted 5 May 2021stat.ME cs.LG stat.MLcited by 62

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Abstract (arXiv)

Introduced more than a half century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience. Despite this popularity, the validity of this notion for inferring causal relationships among time series has remained the topic of continuous debate. Moreover, while the original definition was general, limitations in computational tools have primarily limited the applications of Granger causality to simple bivariate vector auto-regressive processes or pairwise relationships among a set of variables. Starting with a review of early developments and debates, this paper discusses recent advances that address various shortcomings of the earlier approaches, from models for high-dimensional time series to more recent developments that account for nonlinear and non-Gaussian observations and allow for sub-sampled and mixed frequency time series.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). Builds on 21 of its 135 references that are in the map. First posted on arXiv 5 May 2021.

Builds on

21 of 135 references.

References · 21

  1. 2018Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systemscited by 88
  2. 2016Learning Granger Causality for Hawkes Processescited by 68
  3. 2013Regularized estimation in sparse high-dimensional time series modelscited by 40
  4. 2017Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecastingcited by 36
  5. 2017Long-term Forecasting using Higher Order Tensor RNNscited by 34
  6. 2018Neural Granger Causalitycited by 19
  7. 2017In reply to Faes et al. and Barnett et al. regarding "A study of problems encountered in Granger causality analysis from a neuroscience perspective"cited by 14
  8. 2016Causal Discovery from Subsampled Time Series Data by Constraint Optimizationcited by 12
  9. 2018Low Rank and Structured Modeling of High-dimensional Vector Autoregressionscited by 12
  10. 2016Network Reconstruction From High Dimensional Ordinary Differential Equationscited by 11
  11. 2017The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitationcited by 9
  12. 2017Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Modelscited by 9
9 more
  1. 2017Identifiability and Estimation of Structural Vector Autoregressive Models for Subsampled and Mixed Frequency Time Seriescited by 7
  2. 2015A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equationscited by 5
  3. 2020Statistical Inference for Networks of High-Dimensional Point Processescited by 5
  4. 2016Inference of High-dimensional Autoregressive Generalized Linear Modelscited by 4
  5. 2016Bayesian nonparametric sparse VAR modelscited by 4
  6. 2018Hierarchical Attention-Based Recurrent Highway Networks for Time Series Predictioncited by 3
  7. 2013Spatio-Temporal Low Count Processes with Application to Violent Crime Eventscited by 1
  8. 2018Testing for high-dimensional network parameters in auto-regressive modelscited by 1
  9. 2020Confidence intervals for parameters in high-dimensional sparse vector autoregressioncited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 8; 2023: 8; 2024: 18; 2025: 15; 2026: 10.

62 papers.

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  1. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  2. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  3. 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoningcited by 23
  4. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  5. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  6. 2021DIGRAC: Digraph Clustering Based on Flow Imbalancecited by 15
  7. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  8. 2022STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graphcited by 12
  9. 2022Lead-lag detection and network clustering for multivariate time series with an application to the US equity marketcited by 11
  10. 2022Granger causal inference on DAGs identifies genomic loci regulating transcriptioncited by 8
  11. 2023CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boostingcited by 7
  12. 2024Learning Granger Causality from Instance-wise Self-attentive Hawkes Processescited by 7
38 more
  1. 2022Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processescited by 6
  2. 2022Causal inference in drug discovery and developmentcited by 6
  3. 2023Causal Inference for Banking Finance and Insurance A Surveycited by 5
  4. 2024CausalMMM: Learning Causal Structure for Marketing Mix Modelingcited by 5
  5. 2025MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoningcited by 5
  6. 2024TFT-multi: simultaneous forecasting of vital sign trajectories in the ICUcited by 4
  7. 2022Feature Selection for Fault Detection and Prediction based on Event Log Analysiscited by 3
  8. 2023TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequencescited by 3
  9. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  10. 2025Agentic Personalisation of Cross-Channel Marketing Experiencescited by 3
  11. 2025ELATE: Evolutionary Language model for Automated Time-series Engineeringcited by 3
  12. 2024STARFormer: A Novel Spatio-Temporal Aggregation Reorganization Transformer of FMRI for Brain Disorder Diagnosiscited by 2
  13. 2025Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networkscited by 2
  14. 2025UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systemscited by 2
  15. 2021Causal Discovery in High-Dimensional Point Process Networks with Hidden Nodescited by 1
  16. 2022Information Theoretic Measures of Causal Influences during Transient Neural Eventscited by 1
  17. 2023Causal Graph Discovery from Self and Mutually Exciting Time Seriescited by 1
  18. 2024Bayesian Vector AutoRegression with Factorised Granger-Causal Graphscited by 1
  19. 2024Exploring the dynamic interplay of cognitive load and emotional arousal by using multimodal measurements: Correlation of pupil diameter and emotional arousal in emotionally engaging taskscited by 1
  20. 2024Automated Knowledge Graph Learning in Industrial Processescited by 1
  21. 2024Unified Causality Analysis Based on the Degrees of Freedomcited by 1
  22. 2025Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Datacited by 1
  23. 2025Deep spatio-temporal point processes: Advances and new directionscited by 1
  24. 2025Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Seriescited by 1
  25. 2025Linking Actor Behavior to Process Performance Over Timecited by 1
  26. 2021Causal Graph Discovery from Self and Mutually Exciting Time Seriescited by 0
  27. 2022An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Seriescited by 0
  28. 2023Nonlinear Permuted Granger Causalitycited by 0
  29. 2024A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivitycited by 0
  30. 2024Causal Inference from Slowly Varying Nonstationary Processescited by 0
  31. 2024Robust and highly scalable estimation of directional couplings from time-shifted signalscited by 0
  32. 2024Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networkscited by 0
  33. 2024MotifDisco: Motif Causal Discovery For Time Series Motifscited by 0
  34. 2024Scalable Temporal Anomaly Causality Discovery in Large Systems: Achieving Computational Efficiency with Binary Anomaly Flag Datacited by 0
  35. 2025Structured and sparse partial least squares coherence for multivariate cortico-muscular analysiscited by 0
  36. 2025Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?cited by 0
  37. 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networkscited by 0
  38. 2025Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferencescited by 0

50 most cited of 62 All 62 (JSON)