mlsanityData to 13 Oct 2026

Detecting causal associations in large nonlinear time series datasets

arXiv 1702.07007 ↗First posted 22 Feb 2017stat.ME physics.ao-ph stat.APcited by 242

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Abstract (arXiv)

Identifying causal relationships from observational time series data is a key problem in disciplines such as climate science or neuroscience, where experiments are often not possible. Data-driven causal inference 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 that allows to reconstruct causal networks from large-scale time series datasets. We validate the method on a well-established climatic teleconnection connecting the tropical Pacific with extra-tropical temperatures and using large-scale synthetic datasets mimicking the typical properties of real data. The experiments demonstrate that our method outperforms alternative techniques in detection power from small to large-scale datasets and opens up entirely new possibilities to discover causal networks from time series across a range of research fields.

Key facts. Cited by 242 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 Feb 2017.

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Citing papers per year: 2021: 14; 2022: 23; 2023: 50; 2024: 41; 2025: 45; 2026: 69.

242 papers.

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  1. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 64
  2. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
  3. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 56
  4. 2023Causal Recurrent Variational Autoencoder for Medical Time Series Generationcited by 45
  5. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  6. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 40
  7. 2021Will Artificial Intelligence supersede Earth System and Climate Models?cited by 37
  8. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  9. 2023Discovering Causal Relations and Equations from Datacited by 35
  10. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  11. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  12. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
38 more
  1. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  2. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  3. 2023Assumption violations in causal discovery and the robustness of score matchingcited by 28
  4. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  5. 2021THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequencescited by 19
  6. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
  7. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  8. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  9. 2021DIGRAC: Digraph Clustering Based on Flow Imbalancecited by 15
  10. 2021Beyond Predictions in Neural ODEs: Identification and Interventionscited by 15
  11. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 15
  12. 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
  13. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  14. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  15. 2022Causal Discovery of Dynamic Models for Predicting Human Spatial Interactionscited by 13
  16. 2023Identifiability of total effects from abstractions of time series causal graphscited by 13
  17. 2023Dynamic Causal Graph Convolutional Network for Traffic Predictioncited by 12
  18. 2023DRCFS: Doubly Robust Causal Feature Selectioncited by 12
  19. 2022Lead-lag detection and network clustering for multivariate time series with an application to the US equity marketcited by 11
  20. 2024Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcited by 11
  21. 2022CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methodscited by 10
  22. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  23. 2021Entropy-based Discovery of Summary Causal Graphs in Time Seriescited by 9
  24. 2024Separation-based distance measures for causal graphscited by 9
  25. 2024Jacobian Regularizer-based Neural Granger Causalitycited by 9
  26. 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 9
  27. 2021Time Series Analysis via Network Science: Concepts and Algorithmscited by 8
  28. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  29. 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
  30. 2022Multiscale Causal Structure Learningcited by 7
  31. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  32. 2024Causal Graph Neural Networks for Wildfire Danger Predictioncited by 7
  33. 2024Causal Discovery in Semi-Stationary Time Seriescited by 7
  34. 2025CAUSAL3D: A Comprehensive Benchmark for Causal Learning from Visual Datacited by 7
  35. 2021Interactive Causal Structure Discovery in Earth System Sciencescited by 6
  36. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  37. 2023Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discoverycited by 6
  38. 2023Towards Causal Representations of Climate Model Datacited by 6

50 most cited of 242 All 242 (JSON)