mlsanityData to 7 Oct 2026

Causal Inference for Time series Analysis: Problems, Methods and Evaluation

arXiv 2102.05829 ↗First posted 11 Feb 2021cs.LG stat.MLcited by 42

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Abstract (arXiv)

Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analyze this type of data. Time series data has been also used to study the effect of interventions over time. Moreover, in many fields of science, learning the causal structure of dynamic systems and time series data is considered an interesting task which plays an important role in scientific discoveries. Estimating the effect of an intervention and identifying the causal relations from the data can be performed via causal inference. Existing surveys on time series discuss traditional tasks such as classification and forecasting or explain the details of the approaches proposed to solve a specific task. In this paper, we focus on two causal inference tasks, i.e., treatment effect estimation and causal discovery for time series data, and provide a comprehensive review of the approaches in each task. Furthermore, we curate a list of commonly used evaluation metrics and datasets for each task and provide in-depth insight. These metrics and datasets can serve as benchmarks for research in the field.

Key facts. Cited by 42 papers in the map (data through 7 Oct 2026). Builds on 48 of its 194 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

48 of 194 references.

References · 48

  1. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  2. 2018Deep learning for time series classification: a reviewcited by 575
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  4. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
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  6. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 408
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36 more
  1. 2013Gaussian Process Kernels for Pattern Discovery and Extrapolationcited by 131
  2. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
  3. 2016Stochastic Variational Deep Kernel Learningcited by 92
  4. 2019Deep Factors for Forecastingcited by 86
  5. 2019Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounderscited by 74
  6. 2017Invariant Causal Prediction for Sequential Datacited by 63
  7. 2017Deep Learning for Time-Series Analysiscited by 55
  8. 2019Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causalitycited by 55
  9. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 55
  10. 2019Causal Discovery and Forecasting in Nonstationary Environments with State-Space Modelscited by 45
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  12. 2018A review on distance based time series classificationcited by 40
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  17. 2016A scalable end-to-end Gaussian process adapter for irregularly sampled time series classificationcited by 27
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  25. 2019Multimodal Deep Learning for Finance: Integrating and Forecasting International Stock Marketscited by 15
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 7; 2023: 14; 2024: 8; 2025: 9; 2026: 3.

42 papers.

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  1. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  2. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  3. 2023Discovering Causal Relations and Equations from Datacited by 33
  4. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  5. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  6. 2022Applications of statistical causal inference in software engineeringcited by 12
  7. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  8. 2022Deep Recurrent Modelling of Granger Causality with Latent Confoundingcited by 8
  9. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 7
  10. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  11. 2023Causal Inference for Banking Finance and Insurance A Surveycited by 5
  12. 2024Causality from Bottom to Top: A Surveycited by 5
30 more
  1. 2024CausalMMM: Learning Causal Structure for Marketing Mix Modelingcited by 5
  2. 2024Assessing the Impact of Distribution Shift on Reinforcement Learning Performancecited by 4
  3. 2024Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interferencecited by 4
  4. 2025Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructurescited by 4
  5. 2023BALANCE: Bayesian Linear Attribution for Root Cause Localizationcited by 3
  6. 2023Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inferencecited by 3
  7. 2023Causal Feature Selection via Transfer Entropycited by 3
  8. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 3
  9. 2024Causal Reasoning in Software Quality Assurance: A Systematic Reviewcited by 3
  10. 2022Learning domain-specific causal discovery from time seriescited by 2
  11. 2022Investigating Sindy As a Tool For Causal Discovery In Time Series Signalscited by 2
  12. 2023Causal Temporal Regime Structure Learningcited by 2
  13. 2023Behaviour Modelling of Social Animals via Causal Structure Discovery and Graph Neural Networkscited by 2
  14. 2024Diffusion model for relational inferencecited by 2
  15. 2024Rating Multi-Modal Time-Series Forecasting Models (MM-TSFM) for Robustness Through a Causal Lenscited by 2
  16. 2023Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approachcited by 1
  17. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  18. 2024Semiparametric inference for impulse response functions using double/debiased machine learningcited by 1
  19. 2025STOAT: Spatial-Temporal Probabilistic Causal Inference Networkcited by 1
  20. 2025Personalized Counterfactual Framework: Generating Potential Outcomes from Wearable Datacited by 1
  21. 2025Causal Ordering for Structure Learning from Time Seriescited by 1
  22. 2021Learning Generalized Causal Structure in Time-seriescited by 0
  23. 2025Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Carecited by 0
  24. 2025Creating a Causally Grounded Rating Method for Assessing the Robustness of AI Models for Time-Series Forecastingcited by 0
  25. 2025Causal Identification in Time Series Modelscited by 0
  26. 2025Sequential Treatment Effect Estimation with Unmeasured Confounderscited by 0
  27. 2025Causal Inference in Energy Demand Predictioncited by 0
  28. 2026Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretrainingcited by 0
  29. 2026Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventionscited by 0
  30. 2026AutoCause: A Python framework that automates expert decisions in environmental time-series causal discoverycited by 0