# Algorithms to estimate Shapley value feature attributions

@article{Chen2022AlgorithmsTE, title={Algorithms to estimate Shapley value feature attributions}, author={Hugh Chen and Ian Covert and Scott M. Lundberg and Su-In Lee}, journal={ArXiv}, year={2022}, volume={abs/2207.07605} }

Feature attributions based on the Shapley value are popular for explaining machine learning models; however, their estimation is complex from both a theoretical and computational standpoint. We disentangle this complexity into two factors: (1) the approach to removing feature information, and (2) the tractable estimation strategy. These two factors provide a natural lens through which we can better understand and compare 24 distinct algorithms. Based on the various feature removal approaches…

## 8 Citations

### Feature Importance: A Closer Look at Shapley Values and LOCO

- Economics
- 2023

There is much interest lately in explainability in statistics and machine learning. One aspect of explainability is to quantify the importance of various features (or covariates). Two popular methods…

### On marginal feature attributions of tree-based models

- Computer ScienceArXiv
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It is proved that their marginal Shapley values, or more generally marginal feature attributions obtained from a linear game value, are simple (piecewise-constant) functions with respect to a certain finite partition of the input space determined by the trained model.

### SHAP-IQ: Unified Approximation of any-order Shapley Interactions

- Computer ScienceArXiv
- 2023

This work proposes SHAPley Interaction Quantification (SHAP-IQ), an efficient sampling-based approximator to compute Shapley interactions for all three definitions, as well as all other that satisfy the linearity, symmetry and dummy axiom.

### Approximating the Shapley Value without Marginal Contributions

- Computer Science, EconomicsArXiv
- 2023

This paper proposes with SVARM and Stratified SVARM two parameter-free and domain-independent approximation algorithms based on a representation of the Shapley value detached from the notion of marginal contributions that prove unmatched theoretical guarantees regarding their approximation quality and provide satisfying empirical results.

### Learning to Estimate Shapley Values with Vision Transformers

- Computer ScienceArXiv
- 2022

This work uses an attention masking approach to evaluate ViTs with partial information, and develops a procedure to generate Shapley value explanations via a separate, learned explainer model, which provides more accurate explanations than existing methods for ViTs.

### Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions

- Computer ScienceArXiv
- 2023

It is found that the modeling of explanation shifts can be a better indicator for detecting out-of-distribution model behaviour than state- of-the-art techniques.

### Explanation Shift: Detecting distribution shifts on tabular data via the explanation space

- Computer ScienceArXiv
- 2022

It is found that the modeling of explanation shifts can be a better indicator for the detection of predictive performance changes than state-of-the-art techniques based on repre-sentations of distribution shifts.

### Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features

- Computer Science
- 2023

A novel Monte Carlo sampling algorithm that estimates a wide class of linear game values, as well as coalitional values, for the marginal game based on a given ML model and predictor vector at a reduced complexity that depends linearly on the size of the background dataset.

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