# Computing the Shapley Value of Facts in Query Answering

arXiv: https://arxiv.org/abs/2112.08874 · HTML: https://mlsanity.com/paper/2112.08874/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2021.

## Built on by (most cited first, 11 of 11)

- [SHAP Meets Tensor Networks: Provably Tractable Explanations with Parallelism](https://mlsanity.com/paper/2510.21599/) (2025)
- [Expected Shapley-Like Scores of Boolean Functions: Complexity and Applications to Probabilistic Databases](https://mlsanity.com/paper/2401.06493/) (2024)
- [Shapley Value Computation in Ontology-Mediated Query Answering](https://mlsanity.com/paper/2407.20058/) (2024)
- [Shapley Revisited: Tractable Responsibility Measures for Query Answers](https://mlsanity.com/paper/2503.22358/) (2025)
- [A Comprehensive Study of Shapley Value in Data Analytics](https://mlsanity.com/paper/2412.01460/) (2024)
- [The Causal-Effect Score in Data Management](https://mlsanity.com/paper/2502.02495/) (2025)
- [Causality-Based Scores Alignment in Explainable Data Management](https://mlsanity.com/paper/2503.14469/) (2025)
- [Tractable Responsibility Measures for Ontology-Mediated Query Answering](https://mlsanity.com/paper/2507.23191/) (2025)
- [Database Views as Explanations for Relational Deep Learning](https://mlsanity.com/paper/2509.09482/) (2025)
- [CQD-SHAP: Explainable Complex Query Answering via Shapley Values](https://mlsanity.com/paper/2510.15623/) (2025)
- [RelShap: Relationally Consistent Shapley Explanations](https://mlsanity.com/paper/2608.11508/) (2026)

## Abstract (arXiv)

The Shapley value is a game-theoretic notion for wealth distribution that is nowadays extensively used to explain complex data-intensive computation, for instance, in network analysis or machine learning. Recent theoretical works show that query evaluation over relational databases fits well in this explanation paradigm. Yet, these works fall short of providing practical solutions to the computational challenge inherent to the Shapley computation. We present in this paper two practically effective solutions for computing Shapley values in query answering. We start by establishing a tight theoretical connection to the extensively studied problem of query evaluation over probabilistic databases, which allows us to obtain a polynomial-time algorithm for the class of queries for which probability computation is tractable. We then propose a first practical solution for computing Shapley values that adopts tools from probabilistic query evaluation. In particular, we capture the dependence of query answers on input database facts using Boolean expressions (data provenance), and then transform it, via Knowledge Compilation, into a particular circuit form for which we devise an algorithm for computing the Shapley values. Our second practical solution is a faster yet inexact approach that transforms the provenance to a Conjunctive Normal Form and uses a heuristic to compute the Shapley values. Our experiments on TPC-H and IMDB demonstrate the practical effectiveness of our solutions.