mlsanityData to 7 Oct 2026

DYNOTEARS: Structure Learning from Time-Series Data

arXiv 2002.00498 ↗First posted 2 Feb 2020stat.ML cs.LGcited by 198

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Abstract (arXiv)

We revisit the structure learning problem for dynamic Bayesian networks and propose a method that simultaneously estimates contemporaneous (intra-slice) and time-lagged (inter-slice) relationships between variables in a time-series. Our approach is score-based, and revolves around minimizing a penalized loss subject to an acyclicity constraint. To solve this problem, we leverage a recent algebraic result characterizing the acyclicity constraint as a smooth equality constraint. The resulting algorithm, which we call DYNOTEARS, outperforms other methods on simulated data, especially in high-dimensions as the number of variables increases. We also apply this algorithm on real datasets from two different domains, finance and molecular biology, and analyze the resulting output. Compared to state-of-the-art methods for learning dynamic Bayesian networks, our method is both scalable and accurate on real data. The simple formulation and competitive performance of our method make it suitable for a variety of problems where one seeks to learn connections between variables across time.

Key facts. Cited by 198 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 23; 2023: 38; 2024: 41; 2025: 34; 2026: 45.

198 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  3. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  4. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  5. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 64
  6. 2022Temporally Disentangled Representation Learningcited by 59
  7. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 46
  8. 2023Causal Recurrent Variational Autoencoder for Medical Time Series Generationcited by 44
  9. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  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. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
38 more
  1. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  2. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
  3. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  4. 2023Discovering Causal Relations and Equations from Datacited by 33
  5. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 32
  6. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  7. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  8. 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetscited by 27
  9. 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 24
  10. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  11. 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoningcited by 23
  12. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  13. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 21
  14. 2023Temporally Disentangled Representation Learning under Unknown Nonstationaritycited by 21
  15. 2023Causal Modeling with Stationary Diffusionscited by 19
  16. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
  17. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  18. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  19. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  20. 2021FedDAG: Federated DAG Structure Learningcited by 17
  21. 2024Standardizing Structural Causal Modelscited by 17
  22. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  23. 2021Beyond Predictions in Neural ODEs: Identification and Interventionscited by 15
  24. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  25. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  26. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  27. 2022Learning Latent Causal Dynamicscited by 13
  28. 2024RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Modelcited by 13
  29. 2022Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networkscited by 12
  30. 2024Recursive Causal Discoverycited by 12
  31. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 11
  32. 2021Multi-task Learning of Order-Consistent Causal Graphscited by 10
  33. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  34. 2021Entropy-based Discovery of Summary Causal Graphs in Time Seriescited by 9
  35. 2023On the Identifiability of Switching Dynamical Systemscited by 9
  36. 2024LEMMA-RCA: A Large Multi-modal Multi-domain Dataset for Root Cause Analysiscited by 9
  37. 2023Causal Discovery from Subsampled Time Series with Proxy Variablescited by 8
  38. 2023Learning DAGs from Data with Few Root Causescited by 8

50 most cited of 198 All 198 (JSON)