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The many Shapley values for model explanation

Citations

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Cited by:

  1. Xiaoyu Fang & Lin Ding & Meng Gao, 2025. "Carbon Emissions and Innovation Cities: A SHAP-Model-Based Study on Decoupling Trends and Policy Implications in Coastal China," Sustainability, MDPI, vol. 17(8), pages 1-21, April.
  2. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2021. "Cohort Shapley value for algorithmic fairness," Papers 2105.07168, arXiv.org.
  3. Ren, Peng & Liu, Shuang & Qin, Beining & Chen, Yue & Xu, Qi & He, Peng, 2025. "A novel multimodal deep learning-based direct ridership model for planning-oriented demand prediction in urban rail transit networks," Journal of Transport Geography, Elsevier, vol. 129(C).
  4. Mehmet Güney Celbiş & Pui‐hang Wong & Karima Kourtit & Peter Nijkamp, 2023. "Impacts of the COVID‐19 outbreak on older‐age cohorts in European Labor Markets: A machine learning exploration of vulnerable groups," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 559-584, April.
  5. Julliana Gonçalves Marques & Luiz Affonso Guedes & Márjory Cristiany da Costa Abreu, 2022. "Evaluating Time Influence over Performance of Machine-Learning-Based Diagnosis: A Case Study of COVID-19 Pandemic in Brazil," IJERPH, MDPI, vol. 20(1), pages 1-14, December.
  6. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
  7. Hugh Chen & Scott M. Lundberg & Su-In Lee, 2022. "Explaining a series of models by propagating Shapley values," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
  8. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  9. Il Idrissi, Marouane & Bousquet, Nicolas & Gamboa, Fabrice & Iooss, Bertrand & Loubes, Jean-Michel, 2025. "Hoeffding decomposition of functions of random dependent variables," Journal of Multivariate Analysis, Elsevier, vol. 208(C).
  10. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
  11. Rishabh Kumar & Adriano Koshiyama & Kleyton da Costa & Nigel Kingsman & Marvin Tewarrie & Emre Kazim & Arunita Roy & Philip Treleaven & Zac Lovell, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
  12. Marcus Buckmann & Andreas Joseph, 2022. "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers 984, Bank of England.
  13. Wenguang Zhang & Ting Lei & Yu Gong & Jun Zhang & Yirong Wu, 2022. "Using Explainable Artificial Intelligence to Identify Key Characteristics of Deep Poverty for Each Household," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
  14. Ronald Richman & Mario V. Wuthrich, 2021. "LocalGLMnet: interpretable deep learning for tabular data," Papers 2107.11059, arXiv.org.
  15. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
  16. Disha Bhattacharyya & Sudeep Pradhan & Shabbiruddin, 2023. "Barriers in Replacement of Conventional Vehicles by Electric Vehicles in India: A Decision-Making Approach," International Journal of Decision Support System Technology (IJDSST), IGI Global Scientific Publishing, vol. 15(1), pages 1-20, January.
  17. Kokina, Julia & Blanchette, Shay & Davenport, Thomas H. & Pachamanova, Dessislava, 2025. "Challenges and opportunities for artificial intelligence in auditing: Evidence from the field," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
  18. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
  19. Alireza Rezazadeh & Yasamin Jafarian & Ali Kord, 2022. "Explainable Ensemble Machine Learning for Breast Cancer Diagnosis Based on Ultrasound Image Texture Features," Forecasting, MDPI, vol. 4(1), pages 1-13, February.
  20. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.
  21. Farshad Khavari & Jay Liu, 2024. "A Hydrogen-Integrated Aggregator Model for Managing the Point of Common Coupling Congestion in Green Multi-Microgrids," Energies, MDPI, vol. 17(16), pages 1-20, August.
  22. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
  23. Hu'e Sullivan & Hurlin Christophe & P'erignon Christophe & Saurin S'ebastien, 2022. "Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring," Papers 2212.05866, arXiv.org, revised Jan 2025.
  24. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
  25. Lee Changro, 2022. "Training and Interpreting Machine Learning Models: Application in Property Tax Assessment," Real Estate Management and Valuation, Sciendo, vol. 30(1), pages 13-22, March.
  26. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
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