mlsanityData to 7 Oct 2026

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets

arXiv 2003.03685 ↗First posted 7 Mar 2020stat.ME cs.LG stat.MLcited by 136

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Abstract (arXiv)

The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suffer from low recall and partially inflated false positives for strong autocorrelation which is an ubiquitous challenge in time series. The novel method, PCMCI^+, extends PCMCI [Runge et al., 2019b] to include discovery of contemporaneous links. PCMCI^+ improves the reliability of CI tests by optimizing the choice of conditioning sets and even benefits from autocorrelation. The method is order-independent and consistent in the oracle case. A broad range of numerical experiments demonstrates that PCMCI^+ has higher adjacency detection power and especially more contemporaneous orientation recall compared to other methods while better controlling false positives. Optimized conditioning sets also lead to much shorter runtimes than the PC algorithm. PCMCI^+ can be of considerable use in many real world application scenarios where often time resolutions are too coarse to resolve time delays and strong autocorrelation is present.

Key facts. Cited by 136 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Mar 2020.

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Citing papers per year: 2021: 5; 2022: 10; 2023: 29; 2024: 21; 2025: 34; 2026: 37.

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  1. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  2. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  3. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  4. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 39
  5. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  6. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 35
  7. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  8. 2023Discovering Causal Relations and Equations from Datacited by 33
  9. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  10. 2023Root Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loopscited by 30
  11. 2021Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Datacited by 22
  12. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
38 more
  1. 2023Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localizationcited by 18
  2. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  3. 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
  4. 2023Case Studies of Causal Discovery from IT Monitoring Time Seriescited by 14
  5. 2023Neural Structure Learning with Stochastic Differential Equationscited by 14
  6. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
  7. 2024RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Modelcited by 13
  8. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  9. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  10. 2024Separation-based distance measures for causal graphscited by 9
  11. 2025TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycited by 9
  12. 2023Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithmscited by 8
  13. 2023Bootstrap aggregation and confidence measures to improve time series causal discoverycited by 8
  14. 2022Multiscale Causal Structure Learningcited by 7
  15. 2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
  16. 2021Interactive Causal Structure Discovery in Earth System Sciencescited by 6
  17. 2023Towards Causal Representations of Climate Model Datacited by 6
  18. 2025Learning General Causal Structures with Hidden Dynamic Process for Climate Analysiscited by 6
  19. 2026Interventional Time Series Priors for Causal Foundation Modelscited by 6
  20. 2024Causal Representation Learning in Temporal Data via Single-Parent Decodingcited by 5
  21. 2025SpaceTime: Causal Discovery from Non-Stationary Time Seriescited by 5
  22. 2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 5
  23. 2025Causal Climate Emulation with Bayesian Filteringcited by 5
  24. 2022Causal Discovery using Model Invariance through Knockoff Interventionscited by 4
  25. 2022Causal discovery for time series with latent confounderscited by 4
  26. 2023Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverycited by 4
  27. 2025Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Predictioncited by 4
  28. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  29. 2023Causal Temporal Graph Convolutional Neural Networks (CTGCN)cited by 3
  30. 2023TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequencescited by 3
  31. 2023Causal Feature Selection via Transfer Entropycited by 3
  32. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  33. 2024ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applicationscited by 3
  34. 2024TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidentscited by 3
  35. 2024Sortability of Time Series Datacited by 3
  36. 2024CAnDOIT: Causal Discovery with Observational and Interventional Data from Time-Seriescited by 3
  37. 2025Augur: Modeling Covariate Causal Associations in Time Series via Large Language Modelscited by 3
  38. 2025Causal Intervention Sequence Analysis for Fault Tracking in Radio Access Networkscited by 3

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