Shapley values have emerged as a foundational tool in machine learning (ML) for elucidating model decision-making processes. Despite their widespread adoption and unique ability to satisfy essential explainability axioms, computational challenges persist in their estimation when ($i$) evaluating a model over all possible subset of input feature combinations, ($ii$) estimating model marginals, and ($iii$) addressing variability in explanations. We introduce a novel, self-explaining method that simplifies the computation of Shapley values significantly, requiring only a single forward pass. Recognizing the deterministic treatment of Shapley values as a limitation, we explore incorporating a probabilistic framework to capture the inherent uncertainty in explanations. Unlike alternatives, our technique does not rely directly on the observed data space to estimate marginals; instead, it uses adaptable baseline values derived from a latent, feature-specific embedding space, generated by a novel masked neural network architecture. Evaluations on simulated and real datasets underscore our technique's robust predictive and explanatory performance.

翻译：沙普利值已成为机器学习（ML）中解释模型决策过程的基础工具。尽管其被广泛采用且具有独特能力可满足关键的可解释性公理，但在以下场景中其估计仍面临计算挑战：（i）在输入特征所有可能子集组合上评估模型，（ii）估计模型边际值，以及（iii）处理解释中的变异性。我们提出了一种新颖的自解释方法，可显著简化沙普利值的计算，仅需单次前向传播。鉴于沙普利值的确定性处理存在局限性，我们探索融入概率框架以捕捉解释中的固有不确定性。与替代方法不同，我们的技术不直接依赖观测数据空间来估计边际值，而是利用由新颖掩码神经网络架构生成的潜在、特征特定嵌入空间中的可自适应基线值。在模拟和真实数据集上的评估表明，我们的技术具有稳健的预测和解释性能。