As the use of complex machine learning models continues to grow, so does the need for reliable explainability methods. One of the most popular methods for model explainability is based on Shapley values. There are two most commonly used approaches to calculating Shapley values which produce different results when features are correlated, conditional and marginal. In our previous work, it was demonstrated that the conditional approach is fundamentally flawed due to implicit assumptions of causality. However, it is a well-known fact that marginal approach to calculating Shapley values leads to model extrapolation where it might not be well defined. In this paper we explore the impacts of model extrapolation on Shapley values in the case of a simple linear spline model. Furthermore, we propose an approach which while using marginal averaging avoids model extrapolation and with addition of causal information replicates causal Shapley values. Finally, we demonstrate our method on the real data example.

翻译：随着复杂机器学习模型的广泛应用，对可靠可解释性方法的需求日益增长。Shapley值是目前最流行的模型可解释性方法之一。计算Shapley值存在两种最常用的方法——条件方法与边际方法，当特征存在相关性时，这两种方法会产生不同的结果。我们先前的研究表明，条件方法由于隐含的因果假设而存在根本缺陷。然而众所周知，采用边际方法计算Shapley值会导致模型外推，此时模型可能无法准确定义。本文通过简单的线性样条模型，深入探讨模型外推对Shapley值的影响。进一步地，我们提出一种新方法：该方法在采用边际平均的同时避免模型外推，并通过引入因果信息复现因果Shapley值。最后，我们在实际数据案例中验证了该方法的有效性。