A Comprehensive Study of Shapley Value in Data Analytics

TL;DR

This paper provides a comprehensive analysis of Shapley value applications in data analytics, addressing key challenges, evaluating techniques, and introducing an open-source framework for further research and development.

Contribution

It offers the first detailed study of Shapley value in data analytics, clarifies key variables, challenges, and solutions, and introduces SVBench, a modular framework for SV applications.

Findings

Identifies four main challenges: computation efficiency, approximation error, privacy, interpretability.

Analyzes and compares existing techniques for each challenge.

Highlights limitations and future directions for applying SV in data analytics.

Abstract

Over the recent years, Shapley value (SV), a solution concept from cooperative game theory, has found numerous applications in data analytics (DA). This paper presents the first comprehensive study of SV used throughout the DA workflow, clarifying the key variables in defining DA-applicable SV and the essential functionalities that SV can provide for data scientists. We condense four primary challenges of using SV in DA, namely computation efficiency, approximation error, privacy preservation, and interpretability, disentangle the resolution techniques from existing arts in this field, then analyze and discuss the techniques w.r.t. each challenge and the potential conflicts between challenges.We also implement SVBench, a modular and extensible open-source framework for developing SV applications in different DA tasks, and conduct extensive evaluations to validate our analyses and discussions. Based on the qualitative and quantitative results, we identify the limitations of current efforts for applying SV to DA and highlight the directions of future research and engineering.