SHAP Feature Attribution

SHAP: Shapley Additive Explanations

Ask about this topic

Overview

SHAP feature attribution uses Shapley values from cooperative game theory to allocate a model prediction among input features by comparing predictions across feature subsets. KernelSHAP estimates values for black-box models, while TreeSHAP and DeepSHAP exploit tree and neural-network structure; other variants use sampling, conditional or hierarchical feature groups, and specialized units for images and time series. Researchers evaluate explanations for fidelity to model behavior, stability across samples and retraining, and usefulness in applications such as healthcare, security, and finance. A central tension is that correlated features can make rankings unstable or non-unique, results depend on how missing features are represented, and faithful attribution alone does not establish causal importance.

Latest papers

CardsList

36 papers2 papers in the last four weeks, down 67% on the four weeks before. 0.0% of all new papers.

  1. Clinically Interpretable Biological Age Estimation Using CatBoost and SHAP with Continuous and Categorical Features from NHANES

    Oct 14, 2026Yanni Sun, Zheng Qu, Kevin Ren +1SHAP Feature Attribution

    Northern Center for Learning and Innovation, Middletown, New York, USA

  2. Evaluating the Causal Faithfulness of SHAP as a Candidate Attribution Method for SAE Features

    Oct 13, 2026Samuel Xie, Noura Al-MoubayedFaithfulness of Language Model ExplanationsSHAP Feature Attribution

    Independent · Durham University

  3. When Explanations Cannot Be Read: Measuring and Correcting SHAP and LIME Rendering for Right-to-Left Languages

    Sep 23, 2026Rameesha Zia, Muhammad Shahid Iqbal MalikExplainable Artificial IntelligenceSHAP Feature Attribution

    School of Computing Sciences, Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology, Haripur 22620, Pakistan

  4. MorphoSHAP: Rethinking the Unit of Attribution in Explanation for Deep Visual Models

    Sep 22, 2026Anirudh Prabhakaran, Alexandre Rocchi, Gianni FranchiFeature AttributionSHAP Feature Attribution

    AMIAD, Pˆole Recherche, Palaiseau

  5. Evaluating Explanation Methods by the Predictors They Induce

    Sep 17, 2026Jacob Selbæk, Hugo L. HammerFeature AttributionSHAP Feature Attribution

    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway · Department of Holistic Systems, SimulaMet, Oslo, Norway · Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway

  6. Stable and Faithful Explanations for Knowledge Tracing

    Sep 15, 2026Praveena Padi, Arun Morampudi, Ujval Sai Gopal Irrinki +1Knowledge TracingExplainability Evaluation

    Georgia Institute of Technology, Atlanta, Georgia, USA · Independent Researcher, Atlanta, Georgia, USA · Amazon Web Services, Austin, Texas, USA +1

  7. DynSHAP: Towards Explainable Dynamic Survival Analysis

    Sep 11, 2026Nastasya Anokhina, Jonas Jürß, Pietro LiòFeature AttributionSHAP Feature Attribution

    University of Cambridge

  8. Assessing Alignment and Stability of Feature Importance Explanations via Weight of Evidence

    Aug 31, 2026Eddie Conti, Claudio Daka, Álvaro Parafita +3Feature AttributionSHAP Feature Attribution

    Barcelona Supercomputing Center, Barcelona, Spain · University of Florence, Florence, Italy · SMARTEST Research Center, eCampus University, Novedrate, Italy +2

  9. A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification

    Aug 24, 2026Rosa Elysabeth Ralinirina, Jean Christian Ralaivao, Niaiko Michaël Ralaivao +2Explainability EvaluationBlack-Box Model Auditing

    Doctoral School Modeling–Computer Science, University of Fianarantsoa, Madagascar

  10. Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection

    Aug 11, 2026Abdurrahman TolaySHAP Feature AttributionIoT Intrusion Detection

    Independent Researcher, Istanbul, Türkiye

  11. Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

    Jul 20, 2026Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu +1Feature AttributionPredictive Process Monitoring

    SKEMA Business School, 92156 Suresnes, France · KU Leuven, B-3000 Leuven, Belgium

  12. Explaining Process Control Optimisation Recommendations via GradientSHAP and Implicit Differentiation

    Jul 16, 2026Paul Darm, Cem Alpturk, Kenneth Ulrich +3Gradient-Based AttributionSHAP Feature Attribution

    University of Strathclyde, Glasgow, United Kingdom · Weir, Malmö, Sweden · Weir, Glasgow, United Kingdom

  13. SHAP-Weighted Cross-Modal Expert Fusion for Emotion and Sentiment Recognition: Evidence and Limits

    Jul 9, 2026Adis Alihodzic, Selma Skopljakovic HubljarMixture-of-Experts ModelsMultimodal Emotion Recognition

    Department of Mathematical and Computer Sciences, Faculty of Science, University of Sarajevo, Sarajevo, Bosnia and Herzegovina.

  14. Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

    Jul 2, 2026Mona Rajhans, Vishal KhawareyGradient-Boosted Decision TreesSHAP Feature Attribution

    Palo Alto Networks, Santa Clara, CA, USA · Quicken Inc., Menlo Park, CA, USA

  15. Comparing Post-Hoc Explainable AI Methods for Interpreting Black-Box EEG Models in Depression Detection

    May 27, 2026Antonia Šarčević, Nikolina FridGradient-Based AttributionElectroencephalography

    University of Zagreb Faculty of Electrical Engineering and Computing, Croatia

  16. Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks

    May 22, 2026David Boetius, Shahaf Bassan, Guy Katz +2SHAP Feature AttributionShapley Value Attribution

    University of Konstanz, Konstanz, Germany · Hebrew University of Jerusalem, Jerusalem, Israel · University of St.Gallen, St.Gallen, Switzerland.

  17. Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets

    May 21, 2026Ioannis J. Vourganas, Anna Lito MichalaSHAP Feature AttributionExplainability Evaluation

    Netrity Ltd, Glasgow, UK · University of Glasgow, Glasgow, UK

  18. Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition

    May 11, 2026Yu-Fang Tsai, Yu-Jen Chen, Kok-Hua Tan +5Distribution Shift RobustnessSHAP Feature Attribution

    Department of Kinesiology, National Tsing Hua University, Hsinchu, Taiwan · Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan · Physical Education Office, National Tsing Hua University, Hsinchu, Taiwan +4

  19. Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions

    May 6, 2026Ron Wettenstein, Rory Mitchell, Peng YuNumerical IntegrationSHAP Feature Attribution

    Reichman University, Herzliya, Israel · Nvidia Corporation · Shopify

  20. TabSHAP

    Apr 22, 2026Aryan Chaudhary, Prateek Agarwal, Tejasvi AlladiFeature AttributionSHAP Feature Attribution

    Department of Computer Science Birla Institute of Technology and Science India

  21. Explainable Fall Detection for Elderly Monitoring via Temporally Stable SHAP in Skeleton-Based Human Activity Recognition

    Apr 14, 2026Mohammad Saleh, Azadeh TabatabaeiSHAP Feature AttributionHuman Activity Recognition

    Department of Computer Engineering, University of Science and Culture, Tehran, Iran

  22. Shapley Value-Guided Adaptive Ensemble Learning for Explainable Financial Fraud Detection with U.S. Regulatory Compliance Validation

    Apr 14, 2026Mohammad Nasir Uddin, Md Munna AzizFinancial Fraud DetectionEnsemble Learning

    Visual Data Analyst and Applied AI Researcher, Westcliff University, Irvine, CA, USA · Data Analyst and Applied AI Researcher, Westcliff University, Irvine, CA, USA

  23. Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation

    Apr 13, 2026Ricardo Coimbra Brioso, Giulio Sichili, Damiano Dei +4Explainable Medical Image AnalysisSHAP Feature Attribution

    Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy · Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy · Scuola di Specializzazione di Fisica Medica, Università Degli Studi di Milano, Milan, Italy +1

  24. WOODELF-HD: Efficient Background SHAP for High-Depth Decision Trees

    Apr 12, 2026Ron Wettenstein, Alexander Nadel, Udi BokerSHAP Feature Attribution

    Reichman University, Herzliya, Israel · Faculty of Data and Decision Sciences, Technion, Haifa, Israel

  25. A comparative analysis of machine learning models in SHAP analysis

    Apr 8, 2026Justin Lin, Julia FukuyamaSHAP Feature AttributionShapley Value Attribution

    Department of Mathematics, Indiana University · Department of Statistics, Indiana University

  26. The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity

    Apr 8, 2026Drake Caraker, Bryan Arnold, David RhoadsFeature AttributionSHAP Feature Attribution

    Independent Researchers

  27. Real-Time Explanations for Tabular Foundation Models

    Mar 31, 2026Luan Borges Teodoro Reis Sena, Francisco Galuppo AzevedoNeural Network InterpretabilityTabular Foundation Models

    Kunumi Institute, Belo Horizonte, Minas Gerais, Brazil · Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

  28. First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution

    Mar 22, 2026Drake Caraker, Bryan Arnold, David RhoadsSHAP Feature AttributionGradient-Boosted Decision Trees

    Independent Researchers

  29. xplainfi: Feature Importance and Statistical Inference for Machine Learning in R

    Mar 16, 2026Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2Feature AttributionPerturbation-Based Feature Attribution

  30. Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease

    Mar 6, 2026Pablo Guillén, Enrique Frias-MartinezExplainability EvaluationExplanation Stability

    MIU City University, Miami, FL, USA · Research Institute UNIR iTED, Universidad Internacional de La Rioja (UNIR), Spain