Causal Representation Learning

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Causal representation learning aims to recover meaningful variables and relationships from complex observations, so models can predict what happens under interventions and transfer across changing environments. Approaches combine latent-variable models and nonlinear independent component analysis with disentanglement, causal graphs, and invariant learning across environments, interventions, or time. Work spans identifiability theory and methods for images, scientific time series, graphs, and action-conditioned world models. A central tension is that recovery guarantees often rely on assumptions that real data may violate, while predictive accuracy alone does not ensure that a representation captures causal structure; stronger benchmarks and reproducible evaluations remain important.

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102 papers11 papers in the last four weeks, down 21% on the four weeks before. 0.1% of all new papers.

CausalWAM: Learning the Causes of Actions in World Action Models
Oct 30, 2026Juntao Cheng, Jinzhou Tang, Fan Feng +2World Action Models World Models for Robotics
UIUC · UCSD
Intervene, Calibrate, Discover: Learning Causal Structure from Pixels
Oct 26, 2026Ziyi Ding, Xiaodi Huang, Xianxin Lai +4Causal Representation Learning Causal Discovery
Tsinghua University · INFIFORCE Intelligent Technology, China · Institute of Automation, Chinese Academy of Sciences +2
When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
Oct 21, 2026Cong CaoCausal Effect Estimation Causal Mediation Analysis
Department of Biostatistics, Yale School of Public Health, New Haven, CT 06520, USA · Department of Surgery, Stanford University School of Medicine, Stanford, CA 94305, USA
Causal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
Oct 20, 2026Danru Xu, Aapo Hyvärinen, Sara MagliacaneCausal Discovery Causal Discovery with Latent Confounding
University of Amsterdam · Saarland University · University of Helsinki
Causal Value Attribution of Marketing Labels in LLM-Generated Ads: A Deconfounder Approach
Oct 16, 2026Danhong You, Wei Jiang, Zhiqiang +1Causal Inference Causal Inference with Unobserved Confounding
Eric · Shanghai Jiao Tong University · University of Texas at Dallas
Complete or Sparse: A Tale of Two Identifiabilities
Oct 14, 2026Junze Zhou, David KlindtSparse Coding Representation Identifiability
Cold Spring Harbor Laboratory
When Do Learned Blocks Preserve Causal Meaning? Block Identifiability and Constructive Causal Abstractions
Oct 13, 2026Karim Zaghw, Wieland Brendel, Patrik ReizingerCausal Representation Learning Causal Abstraction
University of Tübingen · ELLIS Institute Tübingen · Max Planck Institute for Intelligent Systems, Tübingen, Germany
HiSTOR: Structured Treatment-outcome Representation Learning for High-Dimensional Longitudinal Data
Oct 13, 2026Chuanqi Lao, Junzhou Huang, Qiao LiuCausal Effect Estimation Individualized Treatment Effect Estimation
Department of Computer Science and Engineering, The University of Texas at Arlington · Department of Biostatistics, Yale School of Public Health, Yale University
CausalDreamer: Learning Predictive World Models with Latent Disentanglement
Oct 8, 2026Prince Jha, Nils Lukas, Kun Zhang +1World Model Learning Causal World Models
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Carnegie Mellon University
CausalBind: Causal Modeling and Learning for Protein-Molecule Virtual Screening
Oct 5, 2026Loka Li, Jin Tian, Kun ZhangCausal Representation Learning Structured Sparsity
Mohamed bin Zayed University of Artificial Intelligence · Carnegie Mellon University
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Oct 5, 2026Emre Acartürk, Pranamya Kulkarni, Puranjay Datta +3Causal Representation Learning Causal Identifiability
Rensselaer Polytechnic Institute Troy, NY, USA · Swiss AI, EPFL Lausanne, Switzerland · University of Delaware Newark, DE, USA
Identifiable World Models from Pretrained Diffusion Representations
Oct 4, 2026Ruchi Sandilya, Conor Liston, Logan GrosenickLatent Dynamics Modeling World Models
Department of Psychiatry and Brain & Mind Research Institute Weill Cornell Medicine, New York, NY, USA
CI-JEPA: A Counterfactual Analysis of Latent Representations in Joint-Embedding Predictive Architectures for Self-Supervised Learning
Oct 4, 2026Mintu Dutta, Ritesh Vyas, Mohendra Roy *Causal Representation Learning Joint-Embedding Predictive Architecture
Department of Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India · Department of Electrical and Electronics Engineering, Birla Institute of Technology & Science, Pilani, Rajasthan, India
Proximal Causal Learning under Unmeasured Confounding
Oct 3, 2026Ying Tang, Yi WangProximal Causal Inference Causal Effect Estimation
Shanghai University of International Business and Economics, Shanghai 201620, China
AVL-JEPA: Preventing Causal Dynamics Information Collapse In Joint Embedding Predictive Architecture World Models
Oct 2, 2026Yikang Qiao, Ling Zhang, Ziying Song +1Joint-Embedding Predictive Architecture Action-Conditioned World Models
Central South University · Yanshan University
Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity
Oct 2, 2026Tatsuya Yamada, Hiroshi Morioka, Yoshinobu KawaharaCausal Representation Learning Time-Series Causal Discovery
The University of Osaka · RIKEN · Shiga University
Structure-agnostic Causal Representation Learning
Oct 1, 2026Arman Behnam, Binghui WangCausal Representation Learning OOD Generalization
Department of Computer Science Illinois Institute of Technology Chicago, IL, USA
Identifying ODEs from Unstructured Data with Causal Representation Learning
Sep 29, 2026Alessandro Trenta, Riccardo Massidda, Davide Bacciu +1Causal Representation Learning Representation Identifiability
Department of Computer Science University of Pisa · Saarland Informatics Campus Saarland University & University of Amsterdam
Abductive World Modeling via Causal Representation Learning
Sep 29, 2026Ziqi Liu, Songhan Yang, Linfan Zhou +4Causal Representation Learning Abductive Reasoning
BAIZ Team
I Act Therefore I Am: When Is JEPA's Action-Conditioning Enough to Learn Causal Mechanisms?
Sep 25, 2026Yuhang Liu, Zhuo Huang, Javen Qinfeng ShiJoint-Embedding Predictive Architecture Causal Representation Learning
Responsible AI Research Centre, Australia
Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models
Sep 25, 2026Shan Zhao, Ilija Trajkovic, Julia Kaltenborn +4Surrogate Modeling Causal Representation Learning
Technical University of Munich Munich, Germany · Karlsruhe Institute of Technology Karlsruhe, Germany · McGill University & Mila Montreal, Canada +1
Causal Retention in Interactive Agents: Interface Factorization and Selective Adaptation
Sep 25, 2026Shengjun Zhang, Tingyi Liu, Dong Xie +3Agent Memory Causal Representation Learning
School of Artificial Intelligence, Hubei University · Key Laboratory of Intelligent Sensing System and Security (Hubei University), Ministry of Education · School of Economics and Management, Wuhan University +2
CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models
Sep 16, 2026Tianbin Liu, Jian Zhu, Taiyi Su +5Causal Representation Learning World Models for Robotics
AIRC, Midea Group · Tongji University
When Correlations Mislead: Confounder-Aware Multi-View Urban Region Representation Learning
Sep 14, 2026Sean Bin Yang, Ying Sun, Zongyi Xu +6Causal Representation Learning Graph Representation Learning
Aalborg University, Aalborg, Denmark · Chongqing University of Posts and Telecommunications, Chongqing, China · East China Normal University, Shanghai, China
InfluenceField: A Differentiable Field with Interventionally Identifiable Causal Structure for Multimodal World Modeling
Sep 7, 2026Zihao Yang, Zijia Wang, Zhiqiu HuangCausal Representation Learning Causal World Models
Mathematical Institute, University of Oxford · Department of Computer Science, University of Oxford · School of Mathematical Sciences, University of Nottingham Ningbo China
Representation Learning for Sample-Efficient CATE Estimation by Leveraging Multiple Outcomes
Sep 5, 2026Maitreyi Swaroop, Shikha Bhat, Samantha Rodriguez +2Causal Effect Estimation Conditional Average Treatment Effect Estimation
Machine Learning Department, Carnegie Mellon University · Department of General Internal Medicine, University of Pittsburgh
CauseCollab: Causal Unified and Modality-Agnostic Network for Heterogeneous Collaborative Perception
Sep 3, 2026Weize Li, Yang Li, Quan Yuan +3Causal Representation Learning Cross-Modal Alignment
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions
Aug 29, 2026Haijie Xu, Chen ZhangCausal Representation Learning Causal Discovery
Department of Industrial Engineering Tsinghua University Beijing 100084, China
A Unifying Perspective on Causal World Models: From Observations to Representations to Structure
Aug 13, 2026Avinash Kori, Fabrizio RussoCausal Representation Learning World Model Learning
Department of Computing, Imperial College London, London, UK
Causal Structure is Inducible but Functionally Decoupled: The Routing/Readout Boundary of a Typed Mechanism Library
Aug 12, 2026Xining XunCausal Representation Learning LLM Auditing
Tsingjiao Information Science (Beijing) Co., Ltd.

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CausalWAM: Learning the Causes of Actions in World Action Models

Juntao Cheng, Jinzhou Tang, Fan Feng, Henrik I. Christensen+1

UIUC · UCSD

World Action Models World Models for Robotics Causal World Models Causal Representation Learning

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World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions. The prediction has to be of the whole scene, whereas only a portion of the scene causes actions. WAMs fitted to demonstrations do not, in general, guarantee that they will generate actions solely from the latent causes of the demonstrated actions (the action-relevant latent variables), as the model could just as well learn to imitate in this way whilst entangling the action-relevant latent variables with the nuisance variables. We therefore separate out what causes action from what prediction depends on. We prove that under a sparsity constraint the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone and the true structure is identified up to a permutation of the latent variables. This requires neither sufficient temporal dependence nor any assumptions on how the unobserved regimes evolve and holds even when the action-relevant latent variables are statistically dependent on the rest. We therefore propose CWAM (Causal WAM), which incorporates the constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged. Experiments on both synthetic data and real-world robots validate the theory as well as showcase the improved efficiency in closed-loop success.