Banzhaf Values for Facts in Query Answering
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Abstract (arXiv)
Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measure of fact contribution, yet its efficient computation for select-project-join-union queries is challenging. In this paper, we introduce three algorithms to compute the Banzhaf value of database facts: an exact algorithm, an anytime deterministic approximation algorithm with relative error guarantees, and an algorithm for ranking and top-k. They have three key building blocks: compilation of query lineage into an equivalent function that allows efficient Banzhaf value computation; dynamic programming computation of the Banzhaf values of variables in a Boolean function using the Banzhaf values for constituent functions; and a mechanism to compute efficiently lower and upper bounds on Banzhaf values for any positive DNF function. We complement the algorithms with a dichotomy for the Banzhaf-based ranking problem: given two facts, deciding whether the Banzhaf value of one is greater than of the other is tractable for hierarchical queries and intractable for non-hierarchical queries. We show experimentally that our algorithms significantly outperform exact and approximate algorithms from prior work, most times up to two orders of magnitude. Our algorithms can also cover challenging problem instances that are beyond reach for prior work.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Aug 2023.
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Citing papers per year: 2024: 2; 2025: 4.
6 papers.
- 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelismcited by 8
- 2024Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databasescited by 6
- 2024Shapley Value Computation in Ontology-Mediated Query Answeringcited by 4
- 2025Shapley Revisited: Tractable Responsibility Measures for Query Answerscited by 4
- 2025The Causal-Effect Score in Data Managementcited by 1
- 2025Causality-Based Scores Alignment in Explainable Data Managementcited by 1
- 2025SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism24 Oct 2025
- 2025Shapley Revisited: Tractable Responsibility Measures for Query Answers28 Mar 2025
- 2025Causality-Based Scores Alignment in Explainable Data Management18 Mar 2025
- 2025The Causal-Effect Score in Data Management4 Feb 2025
- 2024Shapley Value Computation in Ontology-Mediated Query Answering29 Jul 2024
- 2024Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases12 Jan 2024