Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. However, existing XAI methods are not tailored towards models in sensitive domains where one predictor is of special interest, such as a treatment effect in a clinical model, or ethnicity in policy models. We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e.g.~treatment) variable from a complex outcome model. Our approach augments the predictive model with a user-defined directed acyclic graph (DAG). The method then uses the graph alongside on-manifold Shapley values to identify effects along causal pathways whilst maintaining robustness to adversarial attacks. We establish error bounds for the identified path-wise Shapley effects and for Shapley values. We show PWSHAP can perform local bias and mediation analyses with faithfulness to the model. Further, if the targeted variable is randomised we can quantify local effect modification. We demonstrate the resolution, interpretability, and true locality of our approach on examples and a real-world experiment.

翻译：预测性黑箱模型虽可展现高精度，但其不透明性阻碍了在安全关键部署环境中的应用。解释性方法（XAI）可通过提升透明度增强决策信心。然而，现有XAI方法未针对敏感领域模型的特殊需求进行优化——例如临床模型中的治疗效果变量或政策模型中的种族变量。本文提出路径级夏普利效应（PWSHAP）框架，用于评估复杂结果模型中二值变量（如治疗变量）的定向效应。该方法通过用户定义的有向无环图（DAG）增强预测模型，并利用该图与流形夏普利值沿因果路径识别效应，同时保持对抗攻击的鲁棒性。我们建立了路径级夏普利效应与夏普利值的误差界，证明PWSHAP能够实现忠实于模型的局部偏差分析与中介分析。进一步地，若目标变量为随机变量，可量化局部效应修正。通过实例及真实实验验证了该方法的解析能力、可解释性与严格局部性。