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The Shapley Value of Tuples in Query Answering

arXiv 1904.08679 ↗First posted 18 Apr 2019cs.DBcited by 23

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Abstract (arXiv)

We investigate the application of the Shapley value to quantifying the contribution of a tuple to a query answer. The Shapley value is a widely known numerical measure in cooperative game theory and in many applications of game theory for assessing the contribution of a player to a coalition game. It has been established already in the 1950s, and is theoretically justified by being the very single wealth-distribution measure that satisfies some natural axioms. While this value has been investigated in several areas, it received little attention in data management. We study this measure in the context of conjunctive and aggregate queries by defining corresponding coalition games. We provide algorithmic and complexity-theoretic results on the computation of Shapley-based contributions to query answers; and for the hard cases we present approximation algorithms.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Apr 2019.

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Citing papers per year: 2021: 4; 2022: 0; 2023: 4; 2024: 3; 2025: 9; 2026: 3.

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  1. 2021Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applicationscited by 59
  2. 2021On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Resultscited by 32
  3. 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelismcited by 8
  4. 2024Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databasescited by 6
  5. 2023The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Researchcited by 4
  6. 2024Shapley Value Computation in Ontology-Mediated Query Answeringcited by 4
  7. 2025Shapley Revisited: Tractable Responsibility Measures for Query Answerscited by 4
  8. 2023From Database Repairs to Causality in Databases and Beyondcited by 3
  9. 2025From Decision Trees to Boolean Logic: A Fast and Unified SHAP Algorithmcited by 3
  10. 2021Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysiscited by 2
  11. 2023Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligencecited by 2
  12. 2023Attribution-Scores in Data Management and Explainable Machine Learningcited by 2
11 more
  1. 2024A Comprehensive Study of Shapley Value in Data Analyticscited by 2
  2. 2021Data Obsolescence Detection in the Light of Newly Acquired Valid Observationscited by 1
  3. 2025The Causal-Effect Score in Data Managementcited by 1
  4. 2025Causality-Based Scores Alignment in Explainable Data Managementcited by 1
  5. 2025Tractable Responsibility Measures for Ontology-Mediated Query Answeringcited by 1
  6. 2025Database Views as Explanations for Relational Deep Learningcited by 1
  7. 2025InfluenceNet: AI Models for Banzhaf and Shapley Value Predictioncited by 0
  8. 2025CQD-SHAP: Explainable Complex Query Answering via Shapley Valuescited by 0
  9. 2026WOODELF-HD: Efficient Background SHAP for High-Depth Decision Treescited by 0
  10. 2026How Hard is it to Decide if a Fact is Relevant to a Query?cited by 0
  11. 2026RelShap: Relationally Consistent Shapley Explanationscited by 0