Causal Discovery

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Overview

Causal discovery studies how to recover directed relationships among variables when experiments are limited or observational data alone are ambiguous. Methods include conditional-independence tests, score-based and ordering searches, functional models such as additive-noise and LiNGAM, and approaches that use interventions or changes across environments; time-series methods also model lagged effects and feedback. Latent confounding, missing data, nonstationarity, and measurement noise complicate recovery, so results often depend on assumptions and may identify only an equivalence class rather than one graph. Current work seeks methods that scale to large and diverse datasets while quantifying uncertainty and testing robustness, with a continuing challenge of translating benchmark performance into reliable real-world discoveries.

Latest papers

CardsList

222 papers31 papers in the last four weeks, up 72% on the four weeks before. 0.2% of all new papers.

  1. Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions

    Mar 23, 2026Panayiotis Panayiotou, Özgür ŞimşekCausal DiscoveryCausal Identifiability

    Department of Computer Science, University of Bath, UK

  2. On the Number of Conditional Independence Tests in Constraint-based Causal Discovery

    Mar 23, 2026Marc Franquesa Monés, Jiaqi Zhang, Caroline UhlerConstraint-Based Causal DiscoveryCausal Discovery

    Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard · Centre de Formació Interdisciplinària Superior, Universitat Politècnica de Catalunya · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology

  3. A Knowledge-Informed Pretrained Model for Causal Discovery

    Mar 21, 2026Wenbo Xu, Yue He, Yunhai Wang +4Causal Discovery

    Renmin University of China, China · Tsinghua University, China · Zhejiang University, China

  4. Neural Autoregressive Flows for Markov Boundary Learning

    Mar 21, 2026Khoa Nguyen, Bao Duong, Viet Huynh +1Causal Structure LearningCausal Discovery

    Deakin Applied Artificial Intelligence Initiative, Geelong, Australia · Edith Cowan University, Perth, Australia

  5. RECLAIM: Cyclic Causal Discovery Amid Measurement Noise

    Mar 21, 2026Muralikrishnna G. Sethuraman, Faramarz FekriCausal Discovery

    School of Electrical and Computer Engineering, Georgia Institute of Technology

  6. MARLIN: Multi-Agent Reinforcement Learning for Incremental DAG Discovery

    Mar 19, 2026Dong Li, Zhengzhang Chen, Xujiang Zhao +5Causal DiscoveryCausal Structure Learning

    Department of Computer Science, Baylor University · NEC Labs America · School of Computer and Cyber Sciences, Augusta University +2

  7. Cohomological Obstructions to Global Counterfactuals: A Sheaf-Theoretic Foundation for Generative Causal Models

    Mar 18, 2026Rui Wu, Hong Xie, Yongjun LiCausal Counterfactual GenerationCausal Inference

    School of Management, University of Science and Technology of China · School of Computer Science and Engineering, University of Science and Technology of China

  8. Evaluating Causal Discovery Algorithms for Path-Specific Fairness and Utility in Healthcare

    Mar 16, 2026Nitish Nagesh, Elahe Khatibi, Thomas Hughes +3Causal DiscoveryAlgorithmic Fairness

    University of California Irvine · University of Orl´eans, France

  9. HCP-DCNet: A Hierarchical Causal Primitive Dynamic Composition Network for Self-Improving Causal Understanding

    Mar 12, 2026Ming Lei, Shufan Wu, Christophe BaehrCausal ReasoningCausal Abstraction

    Shanghai JiaoTong University, Shanghai · M´et´eo France/CNRS, Universit´e de Toulouse, CNRM UMR 3589, Toulouse, France

  10. Learning Causal Structure of Time Series using Best Order Score Search

    Mar 5, 2026Irene Gema Castillo Mansilla, Urmi NinadTime-Series Causal DiscoveryBayesian Networks

    University of Potsdam, Potsdam, Germany · Department of Computer Science, University of Potsdam, Potsdam, Germany

  11. Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding

    Mar 5, 2026Maximilian Hahn, Alina Zajak, Dominik Heider +1Causal DiscoveryCausal Discovery with Latent Confounding

    University of Münster, Institute of Medical Informatics, Münster, Germany

  12. Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learning

    Mar 5, 2026Haoyue Dai, Immanuel Albrecht, Peter Spirtes +1Causal DiscoveryCausal Discovery with Latent Confounding

    Carnegie Mellon University · FernUniversität in Hagen · MBZUAI

  13. Invariant Causal Routing for Governing Social Norms in Online Market Economies

    Mar 4, 2026Xiangning Yu, Qirui Mi, Xiao Xue +4Causal InterventionAI Agent Governance

    Tianjin University, Tianjin, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China · Peking University, Beijing, China +1

  14. Dictionary Based Pattern Entropy for Causal Direction Discovery

    Mar 4, 2026Harikrishnan N B, Shubham Bhilare, Aditi Kathpalia +1Time-Series Causal DiscoveryCausal Discovery

    Department of Computer Science and Information Systems, BITS Pilani K K Birla Goa Campus, NH 17B, Bypass, Road, Zuarinagar, 403726, Goa, India · Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India · Complex Systems Programme, National Institute of Advanced Studies, IISc Campus, Bengaluru, 560012, Karnataka, India

  15. Scalable Contrastive Causal Discovery under Unknown Soft Interventions

    Mar 3, 2026Mingxuan Zhang, Khushi Desai, Sopho Kevlishvili +1Causal DiscoveryStructural Causal Models

    Department of Systems Biology, Columbia University · Irving Institute for Cancer Dynamics, Columbia University · Department of Computer Science, Columbia University +2

  16. I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables

    Mar 3, 2026Hirofumi Suzuki, Kentaro Kanamori, Takuya Takagi +3Causal DiscoveryCausal Discovery with Latent Confounding

    Fujitsu Limited · Faculty of Data Science, Shiga University · RIKEN AIP +2

  17. Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

    Mar 3, 2026Ziruo Hao, Tao Yang, Xiaofeng Wu +1Causal InferenceTime-Series Causal Discovery

  18. Causal Discovery Should Embrace the Wisdom of the Crowd

    Mar 3, 2026Ryan Feng Lin, Yuantao Wei, Huiling Liao +2Causal DiscoveryDecentralized Learning

    Department of Industrial and Systems Engineering, University of Washington, Seattle, WA 98195, USA · Department of Applied Mathematics, Illinois Institute of Technology · Department of Electrical & Computer Engineering, Texas A&M University

  19. No More Maybe-Arrows: Resolving Causal Uncertainty by Breaking Symmetries

    Mar 1, 2026Tingrui Huang, Devendra Singh DhamiCausal DiscoveryCausal Structure Learning

    Department of Mathematics and Computer Science, Eindhoven University of Technology

  20. Operationalizing Longitudinal Causal Discovery Under Real-World Workflow Constraints

    Feb 27, 2026Tadahisa Okuda, Shohei Shimizu, Thong Pham +2Causal DiscoveryCausal Structure Learning

    Kyoto University Graduate School of Medicine, Kyoto, Japan · SANKEN, The University of Osaka, Osaka, Japan · Faculty of Data Science, Shiga University, Shiga, Japan +3

  21. Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data

    Feb 27, 2026Yoichi ChikaharaCausal DiscoveryCausal Identifiability

    Communication Science Laboratories, NTT, Inc., Kyoto, Japan

  22. Causal Direction from Convergence Time: Faster Training in the True Causal Direction

    Feb 24, 2026Abdulrahman TamimCausal Discovery

    Independent Researcher

  23. DMCD: Semantic-Statistical Framework for Causal Discovery

    Feb 23, 2026Samarth KaPatel, Sofia Nikiforova, Giacinto Paolo Saggese +1Causal DiscoveryCausal Structure Learning

    Causify AI

  24. Rethinking Chronological Causal Discovery with Signal Processing

    Feb 23, 2026Kurt Butler, Damian Machlanski, Panagiotis Dimitrakopoulos +1Causal DiscoveryTime-Series Causal Discovery

    School of Engineering, The University of Edinburgh, Edinburgh, UK · Causality in Healthcare AI Hub (CHAI), UK

  25. VCDF: A Validated Consensus-Driven Framework for Time Series Causal Discovery

    Feb 22, 2026Gene Yu, Ce Guo, Wayne LukTime-Series Causal DiscoveryCausal Discovery

    Department of Computing, Imperial College London, London, United Kingdom

  26. Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approach

    Feb 18, 2026Zihao Li, Fabrizio RussoCausal DiscoveryCausal Structure Learning

    Department of Computing, Imperial College London, UK

  27. Sparse Additive Model Pruning for Order-Based Causal Structure Learning

    Feb 17, 2026Kentaro Kanamori, Hirofumi Suzuki, Takuya TakagiCausal Structure LearningCausal Discovery

    Artificial Intelligence Laboratory, Fujitsu Limited

  28. Use What You Know: Causal Foundation Models with Partial Graphs

    Feb 16, 2026Arik Reuter, Anish Dhir, Cristiana Diaconu +6Causal Foundation ModelsCausal Discovery

    University of Cambridge, Cambridge, United Kingdom · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Gatsby Computational Neuroscience Unit, University College London +5

  29. Uncertainty Quantification in Federated Granger Causality Learning

    Feb 13, 2026Ayush Mohanty, Nazal Mohamed, Nagi GebraeelTime-Series Causal DiscoveryCausal Discovery

    Georgia Institute of Technology Atlanta, GA 30332

  30. CauScale: Neural Causal Discovery at Scale

    Feb 9, 2026Bo Peng, Sirui Chen, Jiaguo Tian +2Efficient Neural Network InferenceCausal Discovery

    Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory · Shanghai Innovation Institute +1