Algorithms to estimate Shapley value feature attributions

  title={Algorithms to estimate Shapley value feature attributions},
  author={Hugh Chen and Ian Covert and Scott M. Lundberg and Su-In Lee},
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… 

Feature Importance: A Closer Look at Shapley Values and LOCO

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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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The axiomatic approach is used to study the differences between some of the many operationalizations of the Shapley value for attribution, and a technique called Baseline Shapley (BShap) is proposed that is backed by a proper uniqueness result.

L-Shapley and C-Shapley: Efficient Model Interpretation for Structured Data

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Sampling Permutations for Shapley Value Estimation

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A Multilinear Sampling Algorithm to Estimate Shapley Values

  • Ramin OkhratiAldo Lipani
  • Computer Science, Economics
    2020 25th International Conference on Pattern Recognition (ICPR)
  • 2021
This work proposes a new sampling method based on a multilinear extension technique as applied in game theory that is applicable to any machine learning model, in particular for either multiclass classifications or regression problems.

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