mlsanityData to 7 Oct 2026

CUTS: Neural Causal Discovery from Irregular Time-Series Data

arXiv 2302.07458 ↗First posted 15 Feb 2023cs.LG stat.MEcited by 32

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Abstract (arXiv)

Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when encountering data with randomly missing entries or non-uniform sampling frequencies, which hampers their applications in real scenarios. To address this issue, here we present CUTS, a neural Granger causal discovery algorithm to jointly impute unobserved data points and build causal graphs, via plugging in two mutually boosting modules in an iterative framework: (i) Latent data prediction stage: designs a Delayed Supervision Graph Neural Network (DSGNN) to hallucinate and register unstructured data which might be of high dimension and with complex distribution; (ii) Causal graph fitting stage: builds a causal adjacency matrix with imputed data under sparse penalty. Experiments show that CUTS effectively infers causal graphs from unstructured time-series data, with significantly superior performance to existing methods. Our approach constitutes a promising step towards applying causal discovery to real applications with non-ideal observations.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 15 Feb 2023.

Builds on

24 of 49 references.

References · 24

  1. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  2. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 288
  3. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  4. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  5. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  6. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
  7. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 93
  8. 2022Deep End-to-end Causal Inferencecited by 93
  9. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 82
  10. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  11. 2017Conditional independence testing based on a nearest-neighbor estimator of conditional mutual informationcited by 70
  12. 2019Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causalitycited by 55
12 more
  1. 2018Causal Discovery in the Presence of Missing Datacited by 46
  2. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  3. 2020Causal Discovery from Incomplete Data: A Deep Learning Approachcited by 26
  4. 2017Fast Causal Inference with Non-Random Missingness by Test-Wise Deletioncited by 22
  5. 2021Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variablescited by 16
  6. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  7. 2016Causal Discovery from Subsampled Time Series Data by Constraint Optimizationcited by 12
  8. 2017The Statistical Recurrent Unitcited by 10
  9. 2022Granger causal inference on DAGs identifies genomic loci regulating transcriptioncited by 8
  10. 2018Complete Inference of Causal Relations between Dynamical Systemscited by 7
  11. 2020Causal Discovery from Incomplete Data using An Encoder and Reinforcement Learningcited by 7
  12. 2022Causal Discovery from Sparse Time-Series Data Using Echo State Networkcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 9; 2025: 10; 2026: 9.

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  1. 2024Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causalitycited by 42
  2. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 39
  3. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  4. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  5. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  6. 2024Jacobian Regularizer-based Neural Granger Causalitycited by 9
  7. 2025Learning General Causal Structures with Hidden Dynamic Process for Climate Analysiscited by 6
  8. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  9. 2025Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detectioncited by 5
  10. 2023CTP:A Causal Interpretable Model for Non-Communicable Disease Progression Predictioncited by 2
  11. 2024Diffusion model for relational inferencecited by 2
  12. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
20 more
  1. 2026Coarse-to-Fine Learning of Dynamic Causal Structurescited by 2
  2. 2024Bayesian Vector AutoRegression with Factorised Granger-Causal Graphscited by 1
  3. 2024Discovering Latent Causal Graphs from Spatiotemporal Datacited by 1
  4. 2024Score-matching-based Structure Learning for Temporal Data on Networkscited by 1
  5. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  6. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 1
  7. 2025Transforming Causality: Transformer-Based Temporal Causal Discovery with Prior Knowledge Integrationcited by 1
  8. 2026Position: Universal Time Series Foundation Models Rest on a Category Errorcited by 1
  9. 2024Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networkscited by 0
  10. 2025Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Carecited by 0
  11. 2025Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?cited by 0
  12. 2025A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processescited by 0
  13. 2025Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneitycited by 0
  14. 2026Causal-INSIGHT: Probing Temporal Models to Extract Causal Structurecited by 0
  15. 2026Mask2Cause: Temporal Causal Discovery Beyond Causality in Meancited by 0
  16. 2026Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretrainingcited by 0
  17. 2026Causal Discovery on Irregular Time Seriescited by 0
  18. 2026AutoCause: A Python framework that automates expert decisions in environmental time-series causal discoverycited by 0
  19. 2026ITSY: Causal Discovery From Irregular Time-Series Datacited by 0
  20. 2026seq2cause: One Autoregressive Backbone, Four Causal Discovery Tasks in Event Sequencescited by 0