Causal Discovery with Latent Confounding

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
Sep 29, 2026Sadegh Khorasani, Ali Najar, Saber Salehkaleybar +1Structural Causal Models Causal 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
Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Models
Sep 21, 2026Zijian Li, Ruichu Cai, Feng Xie +7Latent Variable Models Causal Discovery
Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · Carnegie Mellon University, Pittsburgh, PA, USA · Guangdong University of Technology, Guangzhou, China +3
Provable Guarantees and Efficient Learning of Structural Equation Models with Latent Confounders
Sep 16, 2026Weijian Yu, Jean HonorioStructural Causal Models Low-Rank Matrix Decomposition
CIS, The University of Melbourne
Local Causal Structure Learning in the Presence of Latent Variables and Selection Bias
Jul 22, 2026Zheng Li, Hao Zhang, Ruxin Wang +3Causal Discovery Causal 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
Equilibrium Causal Games: Separation, Identification, and the Identifiability of Cyclic Latent States
Jul 21, 2026Faraz Dadgostari, Neda NazemiCausal Representation Learning Causal Identifiability
Department of Mechanical & Industrial Engineering, Montana State University · Gianforte School of Computing, Montana State University
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 Models Bayesian Networks
Department of Information and Computing Sciences, Utrecht University · Leiden Institute of Advanced Computer Science, Leiden University
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
Jul 7, 2026Ming Cai, Hisayuki HaraStructural Causal Models Causal Discovery
Graduate School of Informatics, Kyoto University, Kyoto, Japan · Institute for Liberal Arts and Sciences, Kyoto University, Kyoto, Japan
MDL Meets Latent Confounders: LNML-based Causal Discovery
Jul 5, 2026Zhongyi Que, Shin Matsushima, Kenji YamanishiMinimum Description Length Causal 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
Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signals
Jun 18, 2026Shravan Talupula, Saurabh SharmaCausal Foundation Models Causal Effect Estimation
ProfitOps Inc.
Latent Confounded Causal Discovery via Lie Bracket Geometry
Jun 17, 2026Sridhar MahadevanCausal Discovery Causal Intervention
Adobe Research and University of Massachusetts, Amherst
Identifying Structural Biases from Causal Mechanism Shifts
Jun 17, 2026Praharsh Nanavati, Jilles Vreeken, David KaltenpothStructural Causal Models Causal Discovery
CISPA Helmholtz Center for Information Security
FoundCause: Causal Discovery with Latent Confounders from Observational Data
Jun 16, 2026Patrick Blöbaum, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanStructural Causal Models Amortized Inference
Amazon Web Services · Department of Statistics, University of California, Davis
Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variables
Jun 6, 2026Mariyam Khan, Shohei Shimizu, Thong PhamGraph Structure Learning Causal Discovery
University of Bergen · *Work conducted while a student trainee at RIKEN AIP · 2The University of Osaka +2
Score-Based Causal Discovery of Latent Variable Causal Models
May 19, 2026Ignavier Ng, Xinshuai Dong, Haoyue Dai +3Latent Variable Models Structural Causal Models
Carnegie Mellon University · University of California, San Diego · Mohamed bin Zayed University of Artificial Intelligence
A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variables
May 11, 2026Zheng Li, Feng Xie, Shenglan Nie +3Causal Discovery Causal 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
Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
May 8, 2026Tsuyoshi OkitaAI for Science Causal Discovery
Kyushu Institute of Technology
From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
Apr 24, 2026Zongyu LiCausal Effect Estimation Causal Discovery
Learning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity
Oct 22, 2025Ziyi Zhang, Shaogang Ren, Xiaoning Qian +1Time-Series Causal Discovery Causal Discovery
Texas A&M University · University of Tennessee at Chattanooga · Brookhaven National Laboratory
Design of Experiment for Discovering Directed Mixed Graph
Sep 2, 2025Haijie Xu, Chen ZhangGraph Structure Learning Causal Discovery
Department of Industrial Engineering Tsinghua University
The observational partial order of causal structures with latent variables
Feb 11, 2025Marina Maciel Ansanelli, Elie Wolfe, Robert W. SpekkensLatent Variable Models Causal 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
Towards Complete Causal Explanation with Expert Knowledge
Jul 10, 2024Aparajithan Venkateswaran, Emilija PerkovićStructural Causal Models Causal Discovery
Microsoft · Redmond, WA 98052, USA · Department of Statistics +2
Causal Discovery in Mixtures of Populations
Nov 13, 2023Bijan Mazaheri, Spencer Gordon, Yuval Rabani +1Latent Variable Models Causal Discovery
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire, USA · Fragment Data Technologies, San Francisco, California, USA · Department of Computer Science, Hebrew Univeristy of Jerusalem, Jerusalem, Israel +1

Sep 29, 2026, cs.LGJ/K move · Enter open · S save

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

Sadegh Khorasani, Ali Najar, Saber Salehkaleybar, Negar Kiyavash

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

Structural Causal Models Causal Discovery Causal Structure Learning Causal Identifiability Causal Discovery with Latent Confounding

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We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogenous latent confounders, bounded by a given maximum. We derive the covariance of the observed variables and introduce marginal quasi-equivalence, which characterizes when different causal models share a full-dimensional subset of the observational distributions they can generate. We formulate structure learning as minimization of the Gaussian negative log-likelihood with a logarithmically scaled complexity penalty that counts directed edges and latent variables. For a fixed number of observed variables and a fixed upper bound on latent variables, we establish consistency of global score minimizers up to marginal quasi-equivalence under algebraic faithfulness, structural minimality, and model-overlap assumptions. We parameterize the inclusion of directed edges and candidate latent variables using Bernoulli gates, whose continuous probabilities are optimized jointly with the structural coefficients. Averaging the penalized negative log-likelihood over these gates yields an objective with a closed-form differentiable complexity penalty. We prove that this expected objective has the same global infimum as the corresponding discrete structure-learning objective. Experimental results show that our approach achieves lower recovery error than previous methods in several experimental settings.

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