mlsanityData to 7 Oct 2026

CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery

arXiv 2310.01753 ↗First posted 3 Oct 2023cs.LG stat.MLcited by 35

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Abstract (arXiv)

Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performance on real data. This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation. The pipeline starts from real observations in a specific scenario and produces a matching benchmark dataset. Firstly, we harness deep neural networks along with normalizing flow to accurately capture realistic dynamics. Secondly, we extract hypothesized causal graphs by performing importance analysis on the neural network or leveraging prior knowledge. Thirdly, we derive the ground truth causal graphs by splitting the causal model into causal term, residual term, and noise term. Lastly, using the fitted network and the derived causal graph, we generate corresponding versatile time-series proper for algorithm assessment. In the experiments, we validate the fidelity of the generated data through qualitative and quantitative experiments, followed by a benchmarking of existing TSCD algorithms using these generated datasets. CausalTime offers a feasible solution to evaluating TSCD algorithms in real applications and can be generalized to a wide range of fields. For easy use of the proposed approach, we also provide a user-friendly website, hosted on www.causaltime.cc.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 34 of its 73 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

34 of 73 references.

References · 34

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  2. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
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  8. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  9. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  10. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  11. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  12. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 136
22 more
  1. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
  2. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  3. 2022Deep End-to-end Causal Inferencecited by 93
  4. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
  5. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  6. 2020Causal Discovery in Physical Systems from Videoscited by 77
  7. 2019Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causalitycited by 55
  8. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  9. 2019GRATIS: GeneRAting TIme Series with diverse and controllable characteristicscited by 42
  10. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  11. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 39
  12. 2021Explaining a Series of Models by Propagating Shapley Valuescited by 37
  13. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  14. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 32
  15. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  16. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  17. 2021Towards Generating Real-World Time Series Datacited by 26
  18. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  19. 2022Granger causal inference on DAGs identifies genomic loci regulating transcriptioncited by 8
  20. 2018Complete Inference of Causal Relations between Dynamical Systemscited by 7
  21. 2022Multiscale Causal Structure Learningcited by 7
  22. 2023The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation datacited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 14; 2026: 13.

35 papers.

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  1. 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 24
  2. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  3. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  4. 2025GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causalitycited by 10
  5. 2024Jacobian Regularizer-based Neural Granger Causalitycited by 9
  6. 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 9
  7. 2025Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discoverycited by 7
  8. 2026Interventional Time Series Priors for Causal Foundation Modelscited by 6
  9. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  10. 2025Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detectioncited by 5
  11. 2025Adversarial Causal Tuning for Realistic Time-series Generationcited by 3
  12. 2025DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Seriescited by 3
23 more
  1. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
  2. 2024CausalStock: Deep End-to-end Causal Discovery for News-driven Stock Movement Predictioncited by 2
  3. 2025Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noisescited by 2
  4. 2026Coarse-to-Fine Learning of Dynamic Causal Structurescited by 2
  5. 2026DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Seriescited by 2
  6. 2024Bayesian Vector AutoRegression with Factorised Granger-Causal Graphscited by 1
  7. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 1
  8. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 1
  9. 2025Causal Ordering for Structure Learning from Time Seriescited by 1
  10. 2026Large Causal Models for Temporal Causal Discoverycited by 1
  11. 2024A method to benchmark high-dimensional process drift detectioncited by 0
  12. 2025A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processescited by 0
  13. 2025Robust Causal Discovery in Real-World Time Series with Power-Lawscited by 0
  14. 2025KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamicscited by 0
  15. 2026Transformer Is Inherently a Causal Learnercited by 0
  16. 2026DMCD: Semantic-Statistical Framework for Causal Discoverycited by 0
  17. 2026Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discoverycited by 0
  18. 2026PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discoverycited by 0
  19. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  20. 2026Mask2Cause: Temporal Causal Discovery Beyond Causality in Meancited by 0
  21. 2026Detecting Control and Response Events for AI-Enabled Radio Access Networkscited by 0
  22. 2026AutoCause: A Python framework that automates expert decisions in environmental time-series causal discoverycited by 0
  23. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Eracited by 0