Shapley values are extensively used in explainable artificial intelligence
(XAI) as a framework to explain predictions made by complex machine learning
(ML) models. In this work, we focus on conditional Shapley values for
predictive models fitted to tabular data and explain the prediction
$f(\boldsymbol{x}^{*})$ for a single observation $\boldsymbol{x}^{*}$ at the
time. Numerous Shapley value estimation methods have been proposed and
empirically compared on an average basis in the XAI literature. However, less
focus has been devoted to analyzing the precision of the Shapley value
explanations on an individual basis. We extend our work in Olsen et al. (2023)
by demonstrating and discussing that the explanations are systematically less
precise for observations on the outer region of the training data distribution
for all used estimation methods. This is expected from a statistical point of
view, but to the best of our knowledge, it has not been systematically
addressed in the Shapley value literature. This is crucial knowledge for
Shapley values practitioners, who should be more careful in applying these
observations' corresponding Shapley value explanations.

Shapley value explanations are less precise for observations on the outer region of the training data distribution, which has not been systematically addressed in the Shapley value literature.

个体 Shapley 值解释的精确性

Precision of Individual Shapley Value Explanations

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.

本文主要研究如何使用 Monte Carlo 积分或回归来模拟条件期望值以估计 Shapley 值，以及对现有方法进行改进和系统化分类，并进行广泛的模拟和实际数据实验来评估和推荐何时使用不同的方法类。