Causal Representation Learning

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14 papers in the last four weeks, up 133% on the four weeks before. 0.1% of all new papers.

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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 WangCausal Effect Estimation Causal Inference
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
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
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.
Space as an Interventional Invariant: Cross-Modal Predictive Geometry for Stratified Cities and Em-Spaced Intelligence
Aug 10, 2026Tao Yang, Xuhui Lin, Kunyao Li +1Causal Representation Learning Geospatial Representation Learning
School of Architecture, Tsinghua University, Beijing, China · Department of Geography, University College London, London, United Kingdom · School of Engineering, Cardiff University, Cardiff, United Kingdom
Doubly Robust Functional Representation Learning for Longitudinal Causal Inference with Irregular Histories
Jul 30, 2026Mengfei Ran, Yifeng Shen, Ruijie GuanCausal Representation Learning Causal Inference
Academy of Life and Natural Sciences, Xi’an Jiaotong-Liverpool University, Suzhou, China · Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
Jul 29, 2026Jialu Xu, Mengkun Liang, Guannan Liu +2Representation Learning Causal Representation Learning
MIIT Key Laboratory of Data and Decision Intelligence, Beihang University Beijing, China
Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
Jul 29, 2026Yuhang Jiang, Fengchuan Zhang, Sanguo Zhang +1Causal Representation Learning Domain Adaptation
School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China · Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China
Learning Implicit Causal World Models from Multi-Agent Demonstrations
Jul 28, 2026Jasorsi GhoshCausal Representation Learning Causal World Models
Department of Computer Science Purdue University West Lafayette, Indiana, USA
The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Jul 28, 2026Marie Neubrander, Graham Tierney, Alexander VolfovskyCausal Effect Estimation Causal Representation Learning
Department of Statistical Science Duke University Durham, NC, 27705
CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data
Jul 23, 2026Francis Ndikum Nji, Vandana Janeja, Jianwu WangCausal Representation Learning Clustering
Dept. of Information Systems University of Maryland, Baltimore County Baltimore, MD, U.S.A
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

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CausalDreamer: Learning Predictive World Models with Latent Disentanglement

Prince Jha, Nils Lukas, Kun Zhang, Salem Lahlou

Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) · Carnegie Mellon University

World Model Learning Causal World Models Disentangled Representation Learning Model-Based Planning Causal Representation Learning

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World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world models such as Dreamer 4 consist of a video tokenizer, which encodes each frame into a latent, and a dynamics model, which is pretrained to predict future latents from past latents and actions. Yet the tokenizer is trained with a reconstruction objective, without action or reward supervision, so its latent provides no explicit mechanism to separate controllable, uncontrollable, reward-relevant, and reward-irrelevant information. We propose \textit{CausalDreamer}, which keeps the tokenizer frozen and re-encodes its latent into a factored representation of four groups along two axes: controllability, where only the two controllable groups receive the action, and reward relevance, learned by predicting the reward from the two reward-relevant groups. The pretrained dynamics model is then fine-tuned to predict the factored representation. We evaluate \textit{CausalDreamer} and the pretrained world model it starts from with model-predictive planning on 20 MMBench2 tasks: 10 clean tasks seen during training and 10 unseen tasks, of which 6 are manipulated variants of clean tasks with a changed background, object, or maze layout, and 4 are new environments. We normalize returns so that a policy taking uniformly random actions scores 0 and an expert scores 1. \textit{CausalDreamer} achieves a 14% higher normalized score than the pretrained world model on the clean tasks (0.199 vs.\ 0.175) and a 25% higher score on the manipulated variants (0.307 vs.\ 0.246), while neither model scores meaningfully above the random policy in the new environments. Additionally, our analysis shows that the factored representation separates reward-irrelevant changes, such as a changed background, from its reward-relevant groups.