The Shapley Value of Tuples in Query Answering

Abstract

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.

References in corpus (5)

• Causes and Explanations: A Structural-Model Approach --- Part 1: Causes
• The Shapley Value of Tuples in Query Answering
• From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back
• A Dichotomy for the Generalized Model Counting Problem for Unions of Conjunctive Queries
• The Tractability of SHAP-Score-Based Explanations over Deterministic and Decomposable Boolean Circuits

Cited by in corpus (6)

• The Shapley Value of Tuples in Query Answering
• The Shapley Value of Inconsistency Measures for Functional Dependencies
• The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
• Uniform Reliability of Self-Join-Free Conjunctive Queries
• Relation-Stratified Sampling for Shapley Values Estimation in Relational Databases
• Causality-Based Scores Alignment in Explainable Data Management