Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream business outcomes (e.g. a bank's loan approval rate), so understanding their causes can be crucial. We propose \ours{}: a Shapley value method for attributing prediction shifts to changes in the conditional probabilities of interpretable subgroups of data, where these subgroups are defined by the structure of decision trees. We initially apply this method to single decision trees, providing exact explanations based on conditional probability changes at split nodes. Next, we extend it to tree ensembles by selecting the most explanatory tree and accounting for residual effects. Finally, we propose a model-agnostic variant using surrogate trees grown with a novel objective function, allowing application to models like neural networks. While exact computation can be intensive, approximation techniques enable practical application. We show that \ours{} provides simple, faithful, and near-complete explanations of prediction shifts across model classes, aiding model monitoring in dynamic environments.

翻译：输入分布的变化可能导致机器学习模型平均预测的偏移。此类预测偏移可能影响下游业务成果（例如银行的贷款审批率），因此理解其成因至关重要。我们提出\ours{}：一种用于将预测偏移归因于数据中可解释子组条件概率变化的Shapley值方法，这些子组由决策树的结构定义。我们首先将该方法应用于单一决策树，基于分裂节点的条件概率变化提供精确解释。接着，我们通过选择最具解释性的树并处理残余效应，将其扩展到树集成模型。最后，我们提出一种模型无关的变体，使用基于新型目标函数构建的代理树，使其可应用于神经网络等模型。尽管精确计算可能计算量大，但近似技术能够实现实际应用。我们证明\ours{}可在不同模型类别中提供简单、忠实且近乎完整的预测偏移解释，有助于动态环境中的模型监控。