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. Intervene, Calibrate, Discover: Learning Causal Structure from Pixels

    Oct 26, 2026Ziyi Ding, Xiaodi Huang, Xianxin Lai +4Causal Representation LearningCausal Discovery

    Tsinghua University · INFIFORCE Intelligent Technology, China · Institute of Automation, Chinese Academy of Sciences +2

  2. Operationalising Weaker Faithfulness: Recovery, Ambiguity and Computational Limits

    Oct 22, 2026Cillian Hourican, Rick QuaxConstraint-Based Causal DiscoveryCausal Discovery

    Computational Science Lab, Informatics Institute, University of Amsterdam. · Computational Science Lab, Informatics Institute, University of Amsterdam

  3. 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

  4. Residual-Based Causal Discovery via Pretrained In-Context Quantile Regression

    Oct 21, 2026Daniel Klippert, Quang Nhat Huynh, Alexander MarxCausal Discovery

    Department of Statistics, TU Dortmund University, Germany · Research Center Trustworthy Data Science and Security, University Alliance Ruhr, Germany · Department of Economics, University of Münster, Germany

  5. Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning

    Oct 20, 2026Danru Xu, Aapo Hyvärinen, Sara MagliacaneCausal DiscoveryCausal Discovery with Latent Confounding

    University of Amsterdam · Saarland University · University of Helsinki

  6. Causal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs

    Oct 16, 2026Yang NiCausal DiscoveryCausal Identifiability

    Department of Statistics and Data Science, The University of Texas at Austin

  7. Benchmarking and Enhancing Agentic Causal Discovery

    Oct 16, 2026Pingchuan Ma, Rui Ding, Shuai WangCausal DiscoveryAI Agent Benchmarks

    Zhejiang University of Technology · Microsoft · The Hong Kong University of Science and Technology

  8. Graph Dirichlet Energy for Fitting-Free Bivariate Causal Inference

    Oct 15, 2026Povilas DaniušisCausal Discovery

    Neurotechnology and Vytautas Magnus University

  9. Evaluating Inquiry Learning in LLM-Based Agents: Hypothesizing, Reasoning, and Exploration

    Oct 15, 2026Marta Knežević, Seyed Parsa Neshaei, Jibril Frej +2Instructional ScaffoldingScientific Reasoning in Language Models

    EPFL

  10. Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings

    Oct 13, 2026Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta +1Causal DiscoveryCausal Identifiability

    Oregon State University, Causal Intelligence and Reasoning Lab, Corvallis, USA · RIKEN Advanced Intelligence Project, Causal Inference Team, Tokyo, Japan · The University of Osaka, Reasoning for Intelligence Department, Osaka, Japan +1

  11. Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach

    Oct 13, 2026Yiwen Qiu, Francesco Montagna, Shimeng Huang +1Causal DiscoverySelection Bias

    IST Austria, Am Campus 1, Klosterneuburg, 3400

  12. Searching for What Matters: Open-Ended Discovery of Multiple Hypotheses with Agentic Data Exploration

    Oct 13, 2026Sanchit Kabra, Nurendra Choudhary, Jiangshu Du +3Scientific Hypothesis GenerationData Analysis Agents

    Virginia Tech · Amazon

  13. 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

  14. DAG Structure Learning with Node-Varying Heavy-Tailed Disturbances

    Oct 9, 2026Robin Francis, Sundeep Prabhakar ChepuriCausal Structure LearningCausal Discovery

    Indian Institute of Science, Bangalore, India

  15. Score-Based Learning of Cluster DAGs from Interventions

    Oct 8, 2026Gaetano Tedesco, Alex MarkhamScore-Based Causal DiscoveryCausal Structure Learning

    Informatics Institute, University of Amsterdam · Department of Mathematical Sciences, University of Copenhagen

  16. AdaPS-LiNGAM: Adaptive Predecessor Selection for Linear Non-Gaussian Acyclic Models under Small-Sample Settings

    Oct 7, 2026Shun Yanashima, Kentaro Kanamori, Hirofumi SuzukiCausal DiscoveryCausal Structure Learning

    Artificial Intelligence Laboratory, Fujitsu Limited, 4-1-1 Kamikodanaka, Nakahara-ku, Kawasaki, 211-8588, Kanagawa, Japan.

  17. DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists

    Oct 7, 2026Samuel Margolis, Paul Schmiedmayer, Alan Huang +12Drug DiscoveryAI Agent Benchmarks

    Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA · Department of Medicine, Stanford University, Stanford, CA 94305, USA · Division of Computational Medicine, Department of Medicine, Stanford University, Stanford, CA 94305, USA +1

  18. Directional Evidence Guided Search-Space Reduction for Exact DAG Learning

    Oct 6, 2026Upala Junaida Islam, Abdelmonem Elrefaey, Rong PanScore-Based Causal DiscoveryBayesian Network Structure Learning

    School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA.

  19. GO-Based Clustering for Learning Cluster-Level Causal Gene Regulatory Networks

    Oct 5, 2026Azlaan Mustafa Samad, Wei Zhang, Adèle H RibeiroClusteringCausal Discovery

    L3S Research Center, Leibniz University Hannover, Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed), Hannover, Germany · Institute for Causal and Explainable Artificial Intelligence in Life Sciences, Center for Computational Life Sciences, RWTH Aachen University, Germany

  20. sHAIL-Causal: A Sequential Staircase Procedure for Invariant Causal Predictor Discovery

    Oct 5, 2026Ernest FokouéCausal DiscoveryCausal Inference

    School of Mathematics and Statistics, Rochester Institute of Technology

  21. Coupling Noisy Pairwise Knowledge to the DAG Posterior for Causal Discovery

    Oct 3, 2026Guoliang Xu, James E CorterCausal DiscoveryCausal Structure Learning

    Columbia University

  22. Local Fisher Information Enables Sparse Causal Discovery

    Oct 3, 2026Byeongguk Kang, Donghyeon Lee, Euijong Song +1Causal DiscoveryFisher Information

    Interdisciplinary Program in Artificial Intelligence Seoul National University, Korea · Department of Statistics Seoul National University, Korea · Institute for Data Innovation in Science Seoul National University, Korea

  23. 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

  24. Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery

    Oct 2, 2026Hendrik Suhr, Sascha Xu, Jilles VreekenScore-Based Causal DiscoveryCausal Discovery

    CISPA Helmholtz Center for Information Security

  25. Overcoming Challenges of Interpretive Structural Modeling with Large Language Models

    Sep 30, 2026Everett Rush, David J. Icove, Ari Kim +2Multi-Criteria Decision AnalysisCausal Discovery

    Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN · Oak Ridge National Laboratory, Oak Ridge, TN

  26. 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

  27. Discrete Score Matching Enables Causal Discovery from Count Data

    Sep 30, 2026Euijong Song, Hyewon Park, Gunwoong ParkScore MatchingCausal Discovery

    Department of Statistics Seoul National University, Korea · Interdisciplinary Program in Artificial Intelligence; Institute for Data Innovation in Science, Seoul National University, Korea

  28. Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

    Sep 29, 2026Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1Structural Causal ModelsCausal Discovery

    School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands · College of Management of Technology, EPFL, Lausanne, Switzerland

  29. Demistifying Data and Simulator Assumptions in Supervised Causal Discovery

    Sep 29, 2026Pingchuan Ma, Rui Ding, Bojun Huang +1Causal DiscoveryCausal Structure Learning

    Zhejiang University of Technology, China · Microsoft Research Asia · Sony Research +1

  30. DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery

    Sep 28, 2026Yulong Li, Rong Xia, Yuxuan Zhang +12Causal DiscoveryCausal Attribution

    MBZUAI