A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them

• URL: http://arxiv.org/abs/2305.09536v1
• Date: Tue, 16 May 2023 15:27:17 GMT
• Title: A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
• Authors: Lars Henry Berge Olsen and Ingrid Kristine Glad and Martin Jullum and Kjersti Aas
• Abstract summary: We develop new methods, extend earlier proposed approaches, and systematize the new methods into different method classes for comparison and evaluation. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches.
• Score: 4.3012765978447565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we focus on conditional Shapley values for predictive models fitted to tabular data. Estimating precise conditional Shapley values is difficult as they require the estimation of non-trivial conditional expectations. In this article, we develop new methods, extend earlier proposed approaches, and systematize the new refined and existing methods into different method classes for comparison and evaluation. The method classes use either Monte Carlo integration or regression to model the conditional expectations. We conduct extensive simulation studies to evaluate how precisely the different method classes estimate the conditional expectations, and thereby the conditional Shapley values, for different setups. We also apply the methods to several real-world data experiments and provide recommendations for when to use the different method classes and approaches. Roughly speaking, we recommend using parametric methods when we can specify the data distribution almost correctly, as they generally produce the most accurate Shapley value explanations. When the distribution is unknown, both generative methods and regression models with a similar form as the underlying predictive model are good and stable options. Regression-based methods are often slow to train but produce the Shapley value explanations quickly once trained. The vice versa is true for Monte Carlo-based methods, making the different methods appropriate in different practical situations.

Related papers

• Computing SHAP Efficiently Using Model Structure Information [3.6626323701161665]
  We propose methods that compute SHAP exactly in time or even faster for SHAP definitions that satisfy our additivity and dummy assumptions. For the first case, we demonstrate an additive property and a way to compute SHAP from the lower-order functional components. For the second case, we derive formulas that can compute SHAP in time. Both methods yield exact SHAP results.
  arXiv  Detail & Related papers  (2023-09-05T17:48:09Z)
• Efficient Shapley Values Estimation by Amortization for Text Classification [66.7725354593271]
  We develop an amortized model that directly predicts each input feature's Shapley Value without additional model evaluations. Experimental results on two text classification datasets demonstrate that our amortized model estimates Shapley Values accurately with up to 60 times speedup.
  arXiv  Detail & Related papers  (2023-05-31T16:19:13Z)
• MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation [132.77005365032468]
  We propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE) In our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity.
  arXiv  Detail & Related papers  (2022-05-31T04:57:06Z)
• Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles [0.0]
  We consider the application of Shapley values for explaining decision tree ensembles. We present a novel approach to Shapley value-based feature attribution that can be applied to random forests and boosted decision trees.
  arXiv  Detail & Related papers  (2021-12-16T20:16:02Z)
• Fast Hierarchical Games for Image Explanations [78.16853337149871]
  We present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem.
  arXiv  Detail & Related papers  (2021-04-13T13:11:02Z)
• Explaining predictive models using Shapley values and non-parametric vine copulas [2.6774008509840996]
  We propose two new approaches for modelling the dependence between the features. The performance of the proposed methods is evaluated on simulated data sets and a real data set. Experiments demonstrate that the vine copula approaches give more accurate approximations to the true Shapley values than its competitors.
  arXiv  Detail & Related papers  (2021-02-12T09:43:28Z)
• Distilling Interpretable Models into Human-Readable Code [71.11328360614479]
  Human-readability is an important and desirable standard for machine-learned model interpretability. We propose to train interpretable models using conventional methods, and then distill them into concise, human-readable code. We describe a piecewise-linear curve-fitting algorithm that produces high-quality results efficiently and reliably across a broad range of use cases.
  arXiv  Detail & Related papers  (2021-01-21T01:46:36Z)
• A Multilinear Sampling Algorithm to Estimate Shapley Values [4.771833920251869]
  We propose a new sampling method based on a multilinear extension technique as applied in game theory. Our method is applicable to any machine learning model, in particular for either multi-class classifications or regression problems.
  arXiv  Detail & Related papers  (2020-10-22T21:47:16Z)
• Control as Hybrid Inference [62.997667081978825]
  We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference. We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
  arXiv  Detail & Related papers  (2020-07-11T19:44:09Z)
• Evaluating the Disentanglement of Deep Generative Models through Manifold Topology [66.06153115971732]
  We present a method for quantifying disentanglement that only uses the generative model. We empirically evaluate several state-of-the-art models across multiple datasets.
  arXiv  Detail & Related papers  (2020-06-05T20:54:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.