# groupShapley: Efficient prediction explanation with Shapley values for feature groups

arXiv: https://arxiv.org/abs/2106.12228 · HTML: https://mlsanity.com/paper/2106.12228/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Explaining individual predictions when features are dependent: More accurate approximations to Shapley values](https://mlsanity.com/paper/1903.10464/) (2019)
- [ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R](https://mlsanity.com/paper/1508.04409/) (2015)
- [Feature relevance quantification in explainable AI: A causal problem](https://mlsanity.com/paper/1910.13413/) (2019)
- [Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression](https://mlsanity.com/paper/2012.01536/) (2020)
- [True to the Model or True to the Data?](https://mlsanity.com/paper/2006.16234/) (2020)
- [L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data](https://mlsanity.com/paper/1808.02610/) (2018)
- [Explaining predictive models with mixed features using Shapley values and conditional inference trees](https://mlsanity.com/paper/2007.01027/) (2020)
- [Explaining predictive models using Shapley values and non-parametric vine copulas](https://mlsanity.com/paper/2102.06416/) (2021)
- [Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery](https://mlsanity.com/paper/1812.06181/) (2018)

## Built on by (most cited first, 21 of 21)

- [Explaining Reinforcement Learning with Shapley Values](https://mlsanity.com/paper/2306.05810/) (2023)
- [WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values](https://mlsanity.com/paper/2211.06507/) (2022)
- [Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features](https://mlsanity.com/paper/2111.13507/) (2021)
- [A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them](https://mlsanity.com/paper/2305.09536/) (2023)
- [Efficient XAI Techniques: A Taxonomic Survey](https://mlsanity.com/paper/2302.03225/) (2023)
- [Learning to Understand: Identifying Interactions via the M"obius Transform](https://mlsanity.com/paper/2402.02631/) (2024)
- [On marginal feature attributions of tree-based models](https://mlsanity.com/paper/2302.08434/) (2023)
- [Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing](https://mlsanity.com/paper/2308.16637/) (2023)
- [Shapley Sets: Feature Attribution via Recursive Function Decomposition](https://mlsanity.com/paper/2307.01777/) (2023)
- [Revitalizing Black-Box Interpretability: Actionable Interpretability for LLMs via Proxy Models](https://mlsanity.com/paper/2505.12509/) (2025)
- [Faithful Group Shapley Value](https://mlsanity.com/paper/2505.19013/) (2025)
- [Fast-DataShapley: Neural Modeling for Training Data Valuation](https://mlsanity.com/paper/2506.05281/) (2025)
- [Grouping Shapley Value Feature Importances of Random Forests for explainable Yield Prediction](https://mlsanity.com/paper/2304.07111/) (2023)
- [Group Shapley with Robust Significance Testing and Its Application to Bond Recovery Rate Prediction](https://mlsanity.com/paper/2501.03041/) (2025)
- [GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Series](https://mlsanity.com/paper/2601.06114/) (2026)
- [Do intermediate feature coalitions aid explainability of black-box models?](https://mlsanity.com/paper/2303.11920/) (2023)
- [Succinct Interaction-Aware Explanations](https://mlsanity.com/paper/2402.05566/) (2024)
- [How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features](https://mlsanity.com/paper/2603.05317/) (2026)
- [Conditional Attribution for Root Cause Analysis in Time-Series Anomaly Detection](https://mlsanity.com/paper/2604.17616/) (2026)
- [Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping](https://mlsanity.com/paper/2604.26051/) (2026)
- [CSPF: A Constrained Shared-Private Fusion Method for Non-Verifiable Preference Evaluation](https://mlsanity.com/paper/2607.20862/) (2026)

## Abstract (arXiv)

Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from complex machine learning models. The popularity of Shapley values in the explanation setting is probably due to its unique theoretical properties. The main drawback with Shapley values, however, is that its computational complexity grows exponentially in the number of input features, making it unfeasible in many real world situations where there could be hundreds or thousands of features. Furthermore, with many (dependent) features, presenting/visualizing and interpreting the computed Shapley values also becomes challenging. The present paper introduces groupShapley: a conceptually simple approach for dealing with the aforementioned bottlenecks. The idea is to group the features, for example by type or dependence, and then compute and present Shapley values for these groups instead of for all individual features. Reducing hundreds or thousands of features to half a dozen or so, makes precise computations practically feasible and the presentation and knowledge extraction greatly simplified. We prove that under certain conditions, groupShapley is equivalent to summing the feature-wise Shapley values within each feature group. Moreover, we provide a simulation study exemplifying the differences when these conditions are not met. We illustrate the usability of the approach in a real world car insurance example, where groupShapley is used to provide simple and intuitive explanations.