Causal Discovery with Latent Confounding

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Overview

This field seeks causal graphs and effects from observed variables when unmeasured common causes can create misleading dependencies. Methods use graphical models such as maximal ancestral graphs, conditional-independence and inequality constraints, rank or cumulant structure, and score-based or structural-equation models; many rely on assumptions such as sparsity, linearity, or particular noise distributions. Interventions, differences across environments, and time-series variation can add identifying information, while benchmarks test graph recovery in synthetic and real-world settings, including cyclic systems and selection bias. A central challenge is that distinct causal structures can imply the same observed data, so researchers must determine what is identifiable under each assumption and how to scale discovery without costly interventions.

Latest papers

CardsList

31 papers3 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

  1. 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

  2. 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

  3. 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

  4. 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.

  5. Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models

    Sep 21, 2026Zijian Li, Ruichu Cai, Feng Xie +7Latent Variable ModelsCausal Discovery

    Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Carnegie Mellon University, Pittsburgh, PA, USA · Guangdong University of Technology, Guangzhou, China +3

  6. Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders

    Sep 16, 2026Weijian Yu, Jean HonorioStructural Causal ModelsLow-Rank Matrix Decomposition

    CIS, The University of Melbourne

  7. Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions

    Aug 29, 2026Haijie Xu, Chen ZhangCausal Representation LearningCausal Discovery

    Department of Industrial Engineering Tsinghua University Beijing 100084, China

  8. Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias

    Jul 22, 2026Zheng Li, Hao Zhang, Ruxin Wang +3Causal DiscoveryCausal Structure Learning

    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. · School of Computer Science, Guangdong University of Technology, Guangzhou, China. · Peng Cheng Laboratory, Shenzhen, China. +3

  9. Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States

    Jul 21, 2026Faraz Dadgostari, Neda NazemiCausal Representation LearningCausal Identifiability

    Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University

  10. How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding

    Jul 10, 2026Debargha Ghosh, Silja Renooij, Anna V. KononovaStructural Causal ModelsBayesian Networks

    Department of Information and Computing Sciences, Utrecht University · Leiden Institute of Advanced Computer Science, Leiden University

  11. Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants

    Jul 7, 2026Ming Cai, Hisayuki HaraStructural Causal ModelsCausal Discovery

    Graduate School of Informatics, Kyoto University, Kyoto, Japan · Institute for Liberal Arts and Sciences, Kyoto University, Kyoto, Japan

  12. MDL Meets Latent Confounders: LNML-based Causal Discovery

    Jul 5, 2026Zhongyi Que, Shin Matsushima, Kenji YamanishiMinimum Description LengthCausal Discovery

    Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan · Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan

  13. 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.

  14. Latent Confounded Causal Discovery via Lie Bracket Geometry

    Jun 17, 2026Sridhar MahadevanCausal DiscoveryCausal Intervention

    Adobe Research and University of Massachusetts, Amherst

  15. Identifying Structural Biases from Causal Mechanism Shifts

    Jun 17, 2026Praharsh Nanavati, Jilles Vreeken, David KaltenpothStructural Causal ModelsCausal Discovery

    CISPA Helmholtz Center for Information Security

  16. FoundCause: Causal Discovery with Latent Confounders from Observational Data

    Jun 16, 2026Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanStructural Causal ModelsAmortized Inference

    Amazon Web Services · Department of Statistics, University of California, Davis

  17. Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables

    Jun 6, 2026Mariyam Khan, Shohei Shimizu, Thong PhamGraph Structure LearningCausal Discovery

    University of Bergen · *Work conducted while a student trainee at RIKEN AIP · 2The University of Osaka +2

  18. Score-Based Causal Discovery of Latent Variable Causal Models

    May 19, 2026Ignavier Ng, Xinshuai Dong, Haoyue Dai +3Latent Variable ModelsStructural Causal Models

    Carnegie Mellon University · University of California, San Diego · Mohamed bin Zayed University of Artificial Intelligence

  19. A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables

    May 11, 2026Zheng Li, Feng Xie, Shenglan Nie +3Causal DiscoveryCausal Structure Learning

    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China · Department of Applied Statistics, Beijing Technology and Business University, Beijing, China · College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China

  20. Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science

    May 8, 2026Tsuyoshi OkitaAI for ScienceCausal Discovery

    Kyushu Institute of Technology

  21. From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables

    Apr 24, 2026Zongyu LiCausal Effect EstimationCausal Discovery

  22. 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

  23. 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

  24. 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

  25. Distributed Dynamic Invariant Causal Prediction in Environmental Time Series

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

  26. Traceable Latent Variable Discovery Based on Multi-Agent Collaboration

    Feb 16, 2026Huaming Du, Tao Hu, Yijie Huang +5Causal Discovery with Latent ConfoundingCausal Structure Learning

    Southwestern University of Finance and Economics, Chengdu, Sichuan, China · Hunan University of Technology and Business, Xiangjiang Laboratory, Hunan, China · Emory University, Atlanta, Georgia, United States

  27. Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity

    Oct 22, 2025Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1Time-Series Causal DiscoveryCausal Discovery

    Texas A&M University · University of Tennessee at Chattanooga · Brookhaven National Laboratory

  28. Design of Experiment for Discovering Directed Mixed Graph

    Sep 2, 2025Haijie Xu, Chen ZhangGraph Structure LearningCausal Discovery

    Department of Industrial Engineering Tsinghua University

  29. The observational partial order of causal structures with latent variables

    Feb 11, 2025Marina Maciel Ansanelli, Elie Wolfe, Robert W. SpekkensLatent Variable ModelsCausal Discovery

    Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario Canada N2L 2Y5 · Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

  30. Towards Complete Causal Explanation with Expert Knowledge

    Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićStructural Causal ModelsCausal Discovery

    Microsoft · Redmond, WA 98052, USA · Department of Statistics +2