Time-Series Causal Discovery

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Overview

Time-series causal discovery aims to distinguish directional influences between variables, including effects at different time lags and relationships that change over time. Approaches include Granger-causality tests and vector autoregressions, conditional-independence methods such as PCMCI, score-based and constraint-based graph search, and neural models that learn sparse or changing graphs. Researchers apply these methods to settings such as climate records, clinical measurements, industrial telemetry, and irregular event streams, while developing tools for latent confounding, non-stationarity, and limited observations. A central challenge is that predictive dependence does not by itself establish causation: conclusions rely on assumptions that may fail, and benchmarks must test recovery under realistic violations and interventions.

Latest papers

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65 papers9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

  1. SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning

    Oct 21, 2026Jingbo Wang, Kegeng Tang, Zihao Wang +1Time-Series Causal DiscoveryCausal Discovery

    University of Tennessee at Chattanooga

  2. Where Latents Belong and How They Flow: Joint Identification of Structural Roles

    Oct 16, 2026Xintong Li, Kun Zhang, Yujia ZhengLatent Dynamics ModelingRepresentation Identifiability

    CMU · MBZUAI · UIUC

  3. Beyond RMSE and Granger Gain: Multi-Horizon Forecast-Error Entropy for Nonlinear Predictive Coupling

    Oct 13, 2026Andrei VelichkoTime-Series Causal DiscoveryMultivariate Time Series Forecasting

    Institute of Physics and Technology, Petrozavodsk State University, Petrozavodsk, Russia

  4. One Thing Leads to Another: The Essentials of Time Series Causal Discovery

    Oct 9, 2026Kurt Butler, Damian Machlanski, Michael P. J. Camilleri +5Time-Series Causal DiscoveryCausal Discovery

    School of Engineering, The University of Edinburgh, and the Causality in Healthcare AI Hub (CHAI) · School of Informatics, The University of Edinburgh and CHAI · Department of Statistical Science, University College London, and CHAI

  5. Delay-coordinate reconstruction and conditional-moment causal diagnostics in stochastic systems

    Oct 4, 2026Jun OhkuboDynamical SystemsStochastic Linear Dynamical Systems

    Graduate School of Science and Engineering, Saitama University, Sakura, Saitama, 338–8570 Japan

  6. Causal Lag Structure Discovery in Confounded Time Series via Orthogonalized Adaptive Estimation

    Oct 4, 2026Hong Kiat Tan, Isaac-Neil Zanoria, James Chen +2Debiased MLTime-Series Causal Discovery

    Department of Mathematics, University of California, Los Angeles · Department of Electrical and Computer Engineering, University of California, Los Angeles · Oxford-Man Institute of Quantitative Finance, University of Oxford +1

  7. Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

    Oct 2, 2026Tatsuya Yamada, Hiroshi Morioka, Yoshinobu KawaharaCausal Representation LearningTime-Series Causal Discovery

    The University of Osaka · RIKEN · Shiga University

  8. Inferring Causal Relations between Two Sequences of Events with Language Models

    Sep 30, 2026Nishchal Prasad, Eric Gaussier, Emilie Devijver +3Time-Series Causal DiscoveryCausal Discovery

    INRIA, Univ. Grenoble Alpes, CNRS, LIG · Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG · Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG +1

  9. Forecast-Necessary Causal Discovery for Nonlinear Political Panel Data: Feedback, Functional Form, and the Dynamics of Democratization

    Sep 28, 2026Michael Coppedge, Dmitry Zaytsev, Valentina KuskovaTime-Series Causal DiscoveryCausal Discovery

    Department of Political Science, University of Notre Dame, Notre Dame, IN, USA · Kellogg Institute, University of Notre Dame, Notre Dame, IN, USA · Lucy Family Institute for Data & Society, University of Notre Dame, Notre Dame, IN, USA

  10. Feedback-Robust AI for Patient Knowledge Graphs

    Sep 27, 2026Mohammed Sameer SyedBiomedical KGsKG Construction

    Roshan AI

  11. LUCID: Learning Under Confounding for Inference and Discovery in Time Series

    Sep 25, 2026Mohammad FesangharyTime-Series Causal DiscoveryCausal Inference with Unobserved Confounding

    Bloomberg L.P.

  12. ITSY: Causal Discovery From Irregular Time-Series Data

    Sep 20, 2026Wenbo Xu, Yue He, Yunhai Wang +2Irregular Time-Series ModelingStructural Causal Models

    Renmin University of China, China · Zhejiang University, China

  13. Fundamental Dynamical Units for Physics-Informed Structural Inference from Perturbation Time-Series in Networked Systems

    Sep 14, 2026Nima NouriTime-Series Causal DiscoveryCausal Discovery

    Artificial Intelligence for Science Innovation, AstraZeneca, Waltham, MA, USA

  14. Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets

    Sep 4, 2026Lei Zan, Charles K. Assaad, Emilie Devijver +1Time-Series Causal DiscoveryNon-Stationary Time Series

    Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG, Grenoble, France · Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F75012, Paris, France

  15. Replicating TRACE: A Practitioner's Guide to Its Threshold and Particle Budget

    Sep 1, 2026Alex Chadyuk, Alicia Zhang, Roy KucukatesTime-Series Causal DiscoveryCausal Discovery

    LotusFlare Inc.

  16. Resilient Concurrent Causal Discovery for Topological Event Sequences

    Aug 22, 2026Jiyu Tian, Junhao Dong, Mingchu Li +5Causal DiscoveryTime-Series Causal Discovery

    School of Software Technology, Dalian University of Technology, Dalian, China · College of Computing & Data Science, Nanyang Technological University, Singapore · School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, Jiangxi, China +5

  17. Root cause analysis via difference graph discovery from linear time-series data

    Aug 21, 2026Anouk Ruer, Timothée Loranchet, Daria Bystrova +1Root Cause AnalysisAnomaly Localization

    Sorbonne Université, INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, F75012, Paris, France

  18. Dynamic Structural Causal Modeling for Sleep

    Aug 20, 2026Ranveer Singh, Saurabh Mathur, Pranuthi Tenali +2Time-Series Causal DiscoveryCausal Discovery

    The University of Texas at Dallas, Richardson, USA · TU Darmstadt, Germany · ENT & Sleep Medicine of Dallas, Dallas, USA

  19. Causal Local States: Scalable Simultaneous Causal Network Inference and Forecasting for Dynamical Systems

    Aug 18, 2026Jonas Braun, Fabian Fischbach, Daniel Köglmayr +2Time-Series Causal DiscoveryExplainable Time Series Forecasting

    Ludwig-Maximilians-Universität München, Faculty of Physics, Geschwister-Scholl-Platz 1, 80539 Munich, Germany · Institut für Theoretische Physik, Universität zu Köln, Zülpicher Strasse 77, 50937 Cologne, Germany · Institut für KI-Sicherheit, Deutsches Zentrum für Luft- und Raumfahrt (DLR), St. Augustin & Ulm, Germany +2

  20. AutoCause: A Python framework that automates expert decisions in environmental time-series causal discovery

    Jul 31, 2026Marco Ruiz, Miguel Arana-Catania, David R. Ardila +1Time-Series Causal DiscoveryCausal Discovery

    ISR-Lisbon, Instituto Superior Técnico, Av. Rovisco Pais 1, Lisbon, 1049-001, Portugal · Digital Scholarship at Oxford, University of Oxford, Oxford, OX1 3BG, United Kingdom · Jet Propulsion Lab., Caltech, Pasadena, CA 91109, California, USA

  21. Causal Discovery with Inverted Self-attention for Multivariate Time Series

    Jul 30, 2026Yusen Liu, Yong Wang, Yifan Yin +3Time-Series Causal DiscoveryCausal Discovery

    School of Computer Science, University of Technology Sydney, Sydney, Australia · TianFu YongXing Laboratory, Chengdu, China · Faculty of Data Science, City University of Macau, Macau, China +1

  22. DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series

    Jul 29, 2026Dennis Thumm, Billy Tim Anthony, Ying ChenCounterfactual EvaluationStructural Causal Models

    National University of Singapore, Singapore, Singapore

  23. Causal-TS: A Python Library for Causal Discovery in High-Dimensional and Nonstationary Time Series

    Jul 27, 2026Mohammad FesangharyTime-Series Causal DiscoveryCausal Discovery

    Bloomberg LP, New York, NY, USA

  24. CEDAR: Causal Edge Discovery for Autoregressive Processes

    Jul 22, 2026Mohammad FesangharyTime-Series Causal DiscoveryCausal Discovery

    Bloomberg LP

  25. Causal Discovery on Irregular Time Series

    Jul 20, 2026Martim Penim, Ricardo Ribeiro Pereira, Jacopo Bono +3Irregular Time-Series ModelingTime-Series Causal Discovery

    1Feedzai, Portugal · 3DCC, Faculdade de Ciêncidas da Universidade do Porto, Portugal · 2Instituto Superior Técnico, Universidade de Lisboa, Portugal +1

  26. GRACE: Gated Refinement for Accurate Causal Edge Discovery in High-Dimensional Time Series

    Jun 22, 2026Mohammad Fesanghary, Abhinav HavaldarTime-Series Causal DiscoveryCausal Discovery

  27. Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals

    Jun 18, 2026Shravan Talupula, Saurabh SharmaCausal Foundation ModelsCausal Effect Estimation

    ProfitOps Inc.

  28. Graphical Causal Reasoning for Root Cause Analysis in Cloud Networks

    Jun 11, 2026Fabien Chraim, Dominik Janzing, John EvansTime-Series Causal DiscoveryCloud Computing

    Amazon Web Services

  29. CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts

    Jun 11, 2026Bo Liu, Di Dai, Jingwei Liu +5Time-Series Causal DiscoveryMixture-of-Experts Models

    State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University Beijing, China · National Institute of Health Data Science, and Institute for Artificial Intelligence, Peking University Beijing, China

  30. Learning Temporal Causal Structure via Smooth Differentiable Optimization

    Jun 2, 2026Tong Zhao, Ce Guo, Wayne Luk +2Time-Series Causal DiscoveryCausal Discovery