mlsanityData to 14 Oct 2026

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

arXiv 1905.10857 ↗First posted 26 May 2019cs.LG stat.MLcited by 45

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Abstract (arXiv)

In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study causal discovery and forecasting for nonstationary time series. By exploiting a particular type of state-space model to represent the processes, we show that nonstationarity helps to identify causal structure and that forecasting naturally benefits from learned causal knowledge. Specifically, we allow changes in both causal strengths and noise variances in the nonlinear state-space models, which, interestingly, renders both the causal structure and model parameters identifiable. Given the causal model, we treat forecasting as a problem in Bayesian inference in the causal model, which exploits the time-varying property of the data and adapts to new observations in a principled manner. Experimental results on synthetic and real-world data sets demonstrate the efficacy of the proposed methods.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 May 2019.

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Citing papers per year: 2021: 8; 2022: 5; 2023: 12; 2024: 7; 2025: 8; 2026: 5.

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  1. 2022Temporally Disentangled Representation Learningcited by 61
  2. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  3. 2021Causal Inference for Time series Analysis: Problems, Methods and Evaluationcited by 42
  4. 2023Discovering Causal Relations and Equations from Datacited by 36
  5. 2022Identifying Weight-Variant Latent Causal Modelscited by 34
  6. 2021Action-Sufficient State Representation Learning for Control with Structural Constraintscited by 31
  7. 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetscited by 27
  8. 2021Dynamic Causal Bayesian Optimizationcited by 17
  9. 2023Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounderscited by 17
  10. 2021Optimization-based Causal Estimation from Heterogenous Environmentscited by 16
  11. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 15
  12. 2021Causal Discovery from Conditionally Stationary Time Seriescited by 12
33 more
  1. 2021Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagationcited by 10
  2. 2021Towards Efficient Local Causal Structure Learningcited by 7
  3. 2024Causal Discovery in Semi-Stationary Time Seriescited by 7
  4. 2025Learning General Causal Structures with Hidden Dynamic Process for Climate Analysiscited by 7
  5. 2023Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviourcited by 5
  6. 2023Boosting Differentiable Causal Discovery via Adaptive Sample Reweightingcited by 5
  7. 2023Double Machine Learning Based Structure Identification from Temporal Datacited by 4
  8. 2025Multi-Domain Causal Discovery in Bijective Causal Modelscited by 4
  9. 2022Differentiable Invariant Causal Discoverycited by 3
  10. 2023Estimating Treatment Effects in Continuous Time with Hidden Confounderscited by 3
  11. 2023Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Taskscited by 3
  12. 2025Federated Granger Causality Learning for Interdependent Clients with State Space Representationcited by 3
  13. 2026CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpadcited by 3
  14. 2022Learning domain-specific causal discovery from time seriescited by 2
  15. 2023Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Modelcited by 2
  16. 2024Causal Discovery-Driven Change Point Detection in Time Seriescited by 2
  17. 2021ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptionscited by 1
  18. 2024Identifying Nonstationary Causal Structures with High-Order Markov Switching Modelscited by 1
  19. 2025Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Seriescited by 1
  20. 2025Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Datacited by 1
  21. 2025CausalMamba: Scalable Conditional State Space Models for Neural Causal Inferencecited by 1
  22. 2026Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoningcited by 1
  23. 2022A Survey on Causal Representation Learning and Future Work for Medical Image Analysiscited by 0
  24. 2023MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Schemecited by 0
  25. 2024Invariant Causal Prediction with Local Modelscited by 0
  26. 2024Causal Inference from Slowly Varying Nonstationary Processescited by 0
  27. 2024Spatio-Temporal Graphical Counterfactuals: An Overviewcited by 0
  28. 2024Hybrid Local Causal Discoverycited by 0
  29. 2025Methodological Insights into Structural Causal Modelling and Uncertainty-Aware Forecasting for Economic Indicatorscited by 0
  30. 2025QML-HCS: A Hypercausal Quantum Machine Learning Framework for Non-Stationary Environmentscited by 0
  31. 2026Latent-Space Causal Discovery from Indirect Neuroimaging Observationscited by 0
  32. 2026Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamicscited by 0
  33. 2026Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blanketscited by 0