mlsanityData to 14 Oct 2026

CUTS+: High-dimensional Causal Discovery from Irregular Time-series

arXiv 2305.05890 ↗First posted 10 May 2023cs.LG stat.MEcited by 41

Not on the map. Why?

Abstract (arXiv)

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.

Key facts. Cited by 41 papers in the map (data through 14 Oct 2026). Builds on 26 of its 57 references that are in the map. First posted on arXiv 10 May 2023.

Builds on

26 of 57 references.

References · 26

  1. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,739
  4. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  5. 2018From Coarse to Fine: Robust Hierarchical Localization at Large Scalecited by 559
  6. 2016Recurrent Neural Networks for Multivariate Time Series with Missing Valuescited by 554
  7. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  8. 2018BRITS: Bidirectional Recurrent Imputation for Time Seriescited by 290
  9. 2017Detecting causal associations in large nonlinear time series datasetscited by 244
  10. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  11. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 200
  12. 2020Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetscited by 137
14 more
  1. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 100
  2. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
  3. 2022Deep End-to-end Causal Inferencecited by 95
  4. 2020High-recall causal discovery for autocorrelated time series with latent confounderscited by 84
  5. 2016Modeling Missing Data in Clinical Time Series with RNNscited by 80
  6. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  7. 2019Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causalitycited by 55
  8. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  9. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  10. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
  11. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  12. 2022Granger causal inference on DAGs identifies genomic loci regulating transcriptioncited by 8
  13. 2018Complete Inference of Causal Relations between Dynamical Systemscited by 7
  14. 2022Hankel low-rank approximation and completion in time series analysis and forecasting: a brief reviewcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 14; 2026: 15.

41 papers.

Sort:

By area:

  1. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 36
  2. 2023Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecastingcited by 16
  3. 2024On the Identification of Temporally Causal Representation with Instantaneous Dependencecited by 16
  4. 2024Jacobian Regularizer-based Neural Granger Causalitycited by 9
  5. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 8
  6. 2023Entropy Causal Graphs for Multivariate Time Series Anomaly Detectioncited by 6
  7. 2025Causality-Aware Contrastive Learning for Robust Multivariate Time-Series Anomaly Detectioncited by 6
  8. 2024Learning Flexible Time-windowed Granger Causality Integrating Heterogeneous Interventional Time Series Datacited by 4
  9. 2025DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Seriescited by 4
  10. 2023Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studiescited by 3
  11. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 2
  12. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
29 more
  1. 2025Causal Discovery for Irregularly Time Series with Consistency Guaranteescited by 2
  2. 2025UnCLe: Towards Scalable Dynamic Causal Discovery in Non-linear Temporal Systemscited by 2
  3. 2026Coarse-to-Fine Learning of Dynamic Causal Structurescited by 2
  4. 2024Bayesian Vector AutoRegression with Factorised Granger-Causal Graphscited by 1
  5. 2024Discovering Latent Causal Graphs from Spatiotemporal Datacited by 1
  6. 2025Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Datacited by 1
  7. 2025Time Series Domain Adaptation via Latent Invariant Causal Mechanismcited by 1
  8. 2026PropLLM: Propagation-Aware Scene Reconstruction for Network Fault Diagnosiscited by 1
  9. 2026GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Seriescited by 1
  10. 2026CEDAR: Causal Edge Discovery for Autoregressive Processescited by 1
  11. 2024Causal Discovery from Time-Series Data with Short-Term Invariance-Based Convolutional Neural Networkscited by 0
  12. 2025SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Inputcited by 0
  13. 2025Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Carecited by 0
  14. 2025Emergent Granger Causality in Neural Networks: Can Prediction Alone Reveal Structure?cited by 0
  15. 2025A Gradient-based Causal Discovery Framework with Applications to Complex Industrial Processescited by 0
  16. 2025Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneitycited by 0
  17. 2025KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamicscited by 0
  18. 2025A Self-explainable Model of Long Time Series by Extracting Informative Structured Causal Patternscited by 0
  19. 2026Mask2Cause: Temporal Causal Discovery Beyond Causality in Meancited by 0
  20. 2026Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretrainingcited by 0
  21. 2026CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Expertscited by 0
  22. 2026KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AIcited by 0
  23. 2026Causal Discovery on Irregular Time Seriescited by 0
  24. 2026Resilient Concurrent Causal Discovery for Topological Event Sequencescited by 0
  25. 2026ITSY: Causal Discovery From Irregular Time-Series Datacited by 0
  26. 2026LUCID: Learning Under Confounding for Inference and Discovery in Time Seriescited by 0
  27. 2026Identifiability Guarantees for Drivers and Dynamics of Delayed Physical Systemscited by 0
  28. 2026SFlexRCA: Lightweight, Scalable, and Flexible Root Cause Analysis for IIoT Edge Systemscited by 0
  29. 2026Amortized Counterfactual Prediction with Graph Neural Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026From Causal Discovery to Dynamic Causal Inference in Neural Time Series
  2. 2026CauScale: Neural Causal Discovery at Scale
  3. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discovery
  4. 2024LLM-initialized Differentiable Causal Discovery
  5. 2025Context-Specific Causal Graph Discovery with Unobserved Contexts: Non-Stationarity, Regimes and Spatio-Temporal Patterns
  6. 2026Constraint- and Score-Based Nonlinear Granger Causality Discovery with Kernels
  7. 2025Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
  8. 2024Large Language Models for Constrained-Based Causal Discovery