Counterfactual Learning

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Counterfactual learning asks what would have happened under a different treatment, action, input, or environment, helping researchers estimate interventions, explain predictions, and improve decisions. Methods range from structural causal models and semiparametric estimators to generative models such as flows and diffusion, as well as paired counterfactual rollouts for reinforcement-learning credit assignment. Applications include treatment-effect estimation, fair decision-making, counterfactual explanations, and action-sensitive world models. A central challenge is producing counterfactuals that are both causally identifiable and plausible, then evaluating whether they support reliable decisions rather than merely realistic-looking alternatives.

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80 papers13 papers in the last four weeks, up 30% on the four weeks before. 0.1% of all new papers.

Did the Model Use That? Counterfactual Training Boosts Self-Attribution in Language Models
Oct 26, 2026Zikai Zhou, Jiacheng Sang, Yilin Xu +2Data Provenance in Language Models Counterfactual Learning
Stanford University · Duke University · Emory University +1
Neural-DISCO: Source-Conditioned Counterfactual Editing of Neural Population Activity
Oct 23, 2026Nico Policzer, James Ho, Joseph Soo +1Neural Encoding Counterfactual Learning
Michael Smith Laboratories and Department of Computer Science University of British Columbia Vancouver, BC, Canada
Agents' Last Doubt: Knowing When to Act and When to Step Back in Tool-Using Agents
Oct 16, 2026Qisai Liu, Qingyue Jiao, Bangwei Guo +7Tool-Using Agents LLM Decision-Making
Iowa State University · University of Notre Dame · Rutgers University +2
World Action Critic (WAC) Model: Joint Foresight and Criticism for Policy Learning
Oct 16, 2026Xiangteng Zhang, Haoyuan Liu, Yang Guan +7Action-Conditioned World Models World Models for Autonomous Driving
School of Vehicle and Mobility, Tsinghua University · SunRisingAI Lab · Didi Voyager Labs, Didi Autonomous Driving +1
SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift
Oct 14, 2026Miaobo Hu, Shuhao Hu, Xiaobo Guo +4Imperfect-Information Games Distribution Shift Robustness
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Counterfactual Gate-Support Decomposition: Separating Selection from Redistribution in Training-Data Filtering
Oct 14, 2026Hyojung Han, Jongmin Kim, Seung-Hun JeonTraining Data Selection Counterfactual Learning
ThakiCloud
Evidence-Guided Counterfactual Learning for Long-Tailed Robotic Policies
Oct 9, 2026Weiwei Chen, Chenghao Xu, Meiling Yu +2Robot Policy Learning Robot Learning
Hohai University
RoboAware: Learning to Coordinate Embodied Skills from Counterfactual Outcomes
Oct 8, 2026Bohan Zhou, Xingbei Chen, Emily Huang +17Robot Policy Learning Large Language Model-Based Robot Planning
The Chinese University of Hong Kong · The Hong Kong University of Science and Technology · Knowin AI +3
Counterfactual Route Optimization for Gaussian Head Avatar Modeling
Oct 7, 2026Shikun Zhang, Yong Li, Yiqun Wang +23D Avatar Generation Multi-Objective Optimization
Department of Data Science and AI, Monash University, Australia · College of Computer Science, Chongqing University, China
World Models Dream of Success: Diagnosing and Repairing Failure Insensitivity in Robot World Models
Oct 6, 2026Jiuyi Xu, Xiao Hu, Meida Chen +3World Models for Robotics Action-Conditioned World Models
Colorado School of Mines · Northeastern University · Institute for Creative Technologies, University of Southern California +1
Independent Multi-Agent Reinforcement Learning with Counterfactual Semantic-Social World Models
Oct 6, 2026Fernando Martinez, Tao Li, Yingdong Lu +1Sequential MARL Action-Conditioned World Models
Department of Computer and Information Sciences, Fordham University, New York, NY, 10023, USA · Department of Systems Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China · IBM Research, Yorktown Heights, NY, 10598, USA
Counterfactual Generation via Flow Matching: Coupling-Sensitive End-to-End Rates
Oct 1, 2026Yunrui Guan, Krishnakumar Balasubramanian, Shiva Prasad KasiviswanathanFlow Matching Causal Counterfactual Generation
Department of Applied Mathematics and Statistics, Johns Hopkins University. · Amazon · Department of Statistics, University of California, Davis.
RACE: Relation-Level Counterfactual Explanations for Heterogeneous Graph Neural Networks
Sep 29, 2026Yuxiang Yao, Zijun ZhaoGraph Counterfactual Explanations GNN Explainability
Project Management Department, Research Center, China Life Insurance Company Ltd. · School of Computer Science and Technology, Beijing Institute of Technology
ASCT: Attentive Search over Counterfactual Trees for Credit Assignment in Agentic Reinforcement Learning
Sep 28, 2026Yang Li, Jinhan Yang, hai liu +8Agentic RL Credit Assignment in RL
University of Hong Kong · The Chinese University of Hong Kong · Jiangxi Science and Technology Normal University +1
Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
Sep 27, 2026Yuanhao Li, Hongbo Wang, Xuhong Chen +2RL for Code Generation Software Engineering Agents
State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications, Beijing 100876, China · University of Luxembourg, Luxembourg
OSCC: Certified Observation-Safe Coupling Optimization for Gradient-Noise Control in Imperfect-Information Learning
Sep 27, 2026Miaobo Hu, Shuhao Hu, Xiaobo Guo +4Partially Observable RL Policy Gradient Methods
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China · Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Stable and Counterfactually Robust Physical World Models from Imposed Structure and Learned Physics
Sep 24, 2026Yufeng Wang, Parivesh Priye, Lu Wei +1World Model Learning Physics-Informed ML
Stony Brook University · Georgia Institute of Technology · Westlake University
PCQC: Privileged Counterfactual Question Credit for Multi-Turn Medical Dialogue
Sep 23, 2026Chenxuan Li, Jiayi Wan, Xinrong Chen +3Clinical Reasoning Medical LLMs
Peking University, Beijing, China · Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University, Beijing, China
Dual-Frontier: When Can an Agent Trust Its World Model?
Sep 22, 2026Huatai Zhu, Qiang Chen, Ziqian Kou +5World Model-Based Planning Model-Based Planning
Central South University · The Hong Kong University of Science and Technology · Xiangjiang Laboratory +2
CounterPlay: Counterfactual Post-Training for Self-Play Driving Policies
Sep 18, 2026Jiarong Wei, Yin Wu, Runkai He +1Autonomous Driving RL Post-Training
Department of Computer Science, University of Freiburg, Germany. · CARIAD SE, Germany. · Karlsruhe Institute of Technology, Germany.
Conformal Policy Learning with Distribution-Free Safety Guarantees
Sep 15, 2026Ying Jin, Naoki EgamiConformal Prediction Causal Inference
Department of Statistics and Data Science, University of Pennsylvania · Department of Political Science & Statistics and Data Science Center, Massachusetts Institute of Technology
LPA-CWM: A Learned Physical Adjudicator for Motion Reasoning with Counterfactual World Models
Sep 12, 2026Kunwei Wu, Xiang Liu, Guocai Yao +5Point Tracking Counterfactual Learning
National University of Singapore · Tsinghua University · Beijing Academy of Artificial Intelligence +3
scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning
Sep 9, 2026Murthy DevarakondaSingle-Cell Perturbation Modeling Clinical Outcome Prediction
Causal Data AI (causaldata.ai)
Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
Sep 5, 2026Todd Y. Zhou, Daniel ZhangEmbodied World Models World Model-Based Planning
Harvard University
Counterfactual Quotient Models: Learning What Actions Change, Not What the World Does
Aug 22, 2026Junlin Chen, Ruijie Wang, Jianxin LiAction-Conditioned World Models Model-Based RL
School of Computer Science and Engineering, Beihang University Beijing, China
Learned, Then Lost: A Measured Single-Example Counterfactual in Pre-training
Aug 19, 2026Zachary Speck, Asa ShepardLanguage Model Pretraining Memorization in Language Models
Arizona State University · Williams College
Counterfactual Contrastive Analysis
Aug 19, 2026Yunlong He, Pietro GoriCounterfactual Explanations Image Generation
LTCI, Télécom Paris, Institut Polytechnique de Paris, France
Low-Rank Dynamics-Effective Latent Carriers for Counterfactual Rollout in Learned World Models
Aug 15, 2026Yang Liu, Yuming ChenWorld Model Learning Latent World Models
College of Intelligent Robotics and Advanced Manufacturing, Fudan University Shanghai, China
Command-Space Counterfactual Explanations for Pareto-Conditioned Reinforcement Learning
Aug 15, 2026Joanikij Chulev, Hendrik BaierInterpretability in RL Counterfactual Learning
Centrum Wiskunde & Informatica (CWI), Amsterdam · Eindhoven University of Technology
Chance-constrained selection of sequential intervention strategies from counterfactual estimates
Aug 13, 2026Minkyoung Kim, Beakcheol JangStochastic Optimization Chance-Constrained Optimization
Graduate School of Information, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea

Oct 26, 2026J/K move · Enter open · S save

Did the Model Use That? Counterfactual Training Boosts Self-Attribution in Language Models

Zikai Zhou, Jiacheng Sang, Yilin Xu, Yufei Jin+1

Stanford University · Duke University · Emory University+1

Data Provenance in Language Models Counterfactual Learning Counterfactual Reasoning in Language Models

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A fundamental challenge for explainable and accountable language models is revealing how external information influences their answers. This paper examines whether models are self-attributive: that is, whether they indicate when -- and only when -- their responses to questions are influenced by information provided alongside the question. Furthermore, we explore how self-attribution can be improved through counterfactual training, which explicitly teaches models to distinguish between answers that do and do not depend on supplied information. We found that most of the eleven frontier models rarely attribute influential information in their answers, and the rest attribute non-influential information. Training acknowledgment into reasoning does not fix this: the acknowledgment does not reach the final answer. We introduce CREDIT, which directly supervises final-answer self-attribution with counterfactual labels, without the need for human annotation. CREDIT substantially improves attribution in four open models, including two distilled reasoning models, and outperforms reasoning-level fine-tuning and prompting baselines. Among answers that agree with a user's suggestion, CREDIT distinguishes those changed by the suggestion from those the model would have given anyway. Gains transfer partly to unseen information types and prompts containing sources that support conflicting answers.