Counterfactual Learning

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Overview

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.

Latest papers

CardsList

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

  1. ScriptHOI: Learning Scripted State Transitions for Open-Vocabulary Human-Object Interaction Detection

    May 6, 2026Minh Anh Nguyen, Quang Huy Tran, Bao Ngoc Le +3Counterfactual LearningHuman-Object Interaction

    Phenikaa University

  2. SpecPL: Disentangling Spectral Granularity for Prompt Learning

    May 6, 2026Jingtao Zhou, Xirui Kang, Feiyang Huang +1VLM AdaptationFrequency-Domain Feature Learning

    Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR.

  3. CRAFT: Counterfactual-to-Interactive Reinforcement Fine-Tuning for Driving Policies

    May 6, 2026Keyu Chen, Nanfei Ye, Yida Wang +4Reinforcement LearningAutonomous Driving Planning

    School of Vehicle and Mobility, Tsinghua University · Li Auto Inc

  4. Counterfactual identifiability beyond global monotonicity: non-monotone triangular structural causal models

    May 6, 2026Pengcheng Tan, Jiang Chen, Dehui DuStructural Causal ModelsCausal Counterfactual Generation

    Shanghai Key Laboratory of Trustworthy Computing, East China Normal University

  5. Machine learning models for estimating counterfactuals in a single-arm inflammatory bowel disease study

    Apr 25, 2026Dan Liu, Fida K. Dankar, Jennifer C. deBruyn +4Causal Effect EstimationClinical Outcome Prediction

    Children’s Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada · School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada · Alberta Children’s Hospital, Calgary, Alberta, Canada +3

  6. DeepImagine: Learning Biomedical Reasoning via Successive Counterfactual Imagining

    Apr 24, 2026Youze Zheng, Jianyou Wang, Yuhan Chen +8LLM Fine-TuningCounterfactual Reasoning in Language Models

    Laboratory for Emerging Intelligence, University of California, San Diego · University of Chicago · Elsevier +1

  7. A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations

    Apr 19, 2026Marcin Kostrzewa, Maciej Zięba, Jerzy StefanowskiCounterfactual ExplanationsConditional Generative Modeling

    Wrocław University of Science and Technology, Wrocław 50-347, Poland · Tooploox, Wrocław 53-601, Poland · Poznań University of Technology, Poznań 61-131, Poland

  8. Malliavin Calculus for Counterfactual Gradient Estimation in Adaptive Inverse Reinforcement Learning

    Apr 1, 2026Vikram Krishnamurthy, Luke SnowReinforcement LearningCounterfactual Learning

    School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA

  9. CAETC: Causal Autoencoding and Treatment Conditioning for Counterfactual Estimation over Time

    Mar 12, 2026Nghia D. Nguyen, Pablo Robles-Granda, Lav R. VarshneyCausal Effect EstimationCounterfactual Learning

    Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign · VinUni-Illinois Smart Health Center, VinUniversity · AI Innovation Institute, Stony Brook University +1

  10. AULLM++: Structured-Token-Conditioned Large Language Models for Micro-Expression Action Unit Detection

    Mar 9, 2026Zhishu Liu, Kaishen Yuan, Bo Zhao +2Facial Action Unit DetectionFacial Expression Recognition

    Great Bay University, Dongguan, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

  11. Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks

    Mar 4, 2026Matteo De Sanctis, Riccardo De Sanctis, Stefano Faralli +2GNN ExplainabilityCounterfactual Learning

    Sapienza University of Rome, Rome, Italy · ISTC-CNR & Sapienza University of Rome, Rome, Italy · Technical University of Munich, Munich, Germany

  12. Portfolio Reinforcement Learning with Scenario-Context Rollout

    Feb 27, 2026Vanya Priscillia Bendatu, Yao LuPortfolio OptimizationQuantitative Finance

    National University of Singapore

  13. Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction

    Feb 23, 2026Ha-Anh Hoang Nguyen, Tri-Duc Phan Le, Duc-Hoang Pham +4Counterfactual PredictionCancer Survival Prediction

    University of Engineering and Technology, Hanoi Vietnam National University, Vietnam · Delft University of Technology, the Netherlands

  14. Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

    Feb 20, 2026Simi Job, Xiaohui Tao, Taotao Cai +3Graph Neural NetworksCausal Inference

    School of Mathematics, Physics, and Computing, University of Southern Queensland, Australia · School of Data Science, Lingnan University, Hong Kong · School of Business, University of Southern Queensland, Australia +1

  15. Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

    Jan 21, 2026Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2Counterfactual ExplanationsLLM Fine-Tuning

    College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA. · School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA. · School of Nutritional Sciences and Wellness, University of Arizona, Tucson, AZ 85721, USA.

  16. Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation

    Nov 15, 2025Yannan Chen, Ruoyu Chen, Wei Wang +6Adversarial RobustnessOOD Generalization

    School of Cyber Science and Technology, Sun Yat-Sen University, Guangdong Province, China · Institute of Perception, PC Laboratory, Guangdong Province, China · University of Chinese Academy of Sciences, Beijing, China +3

  17. Reinforcement Learning in MDPs with Information-Ordered Policies

    Aug 5, 2025Zhongjun Zhang, Shipra Agrawal, Ilan Lobel +2Queueing TheoryPolicy Learning

    Department of Industrial Engineering and Management Sciences, Northwestern University · Department of Industrial Engineering and Operations Research, Columbia University · Technology, Operations, and Statistics, Stern School of Business, New York University +1

  18. Semiparametric Inference for Counterfactual Regression under Intervention-Driven Shift

    Apr 3, 2025Kwangho KimDistribution Shift RobustnessSemiparametric Inference

    Department of Statistics, Korea University

  19. SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence

    Dec 6, 2024Bowei Tian, Yexiao He, Ziyao Wang +3Debiased MLAlgorithmic Fairness

    University of Maryland, College Park, MD 20742, USA · Clemson University, Clemson, SC 29634, USA

  20. Hierarchical Deep Counterfactual Regret Minimization

    May 27, 2023Jiayu Chen, Xudong Wu, Zhekai Wang +1Imperfect-Information GamesHierarchical RL

    The University of Hong Kong Hong Kong SAR · Peking University Beijing, China, 100871 · Purdue University2025 West Lafayette, IN 47907