Conformal prediction offers finite-sample coverage guarantees under minimal assumptions. However, existing methods treat the entire modeling process as a black box, overlooking opportunities to exploit modular structure. We introduce a conformal prediction framework for two-stage sequential models, where an upstream predictor generates intermediate representations for a downstream model. By decomposing the overall prediction residual into stage-specific components, our method enables practitioners to attribute uncertainty to specific pipeline stages. We develop a risk-controlled parameter selection procedure using family-wise error rate (FWER) control to calibrate stage-wise scaling parameters, and propose an adaptive extension for non-stationary settings that preserves long-run coverage guarantees. Experiments on synthetic distribution shifts, as well as real-world supply chain and stock market data, demonstrate that our approach maintains coverage under conditions that degrade standard conformal methods, while providing interpretable stage-wise uncertainty attribution. This framework offers diagnostic advantages and robust coverage that standard conformal methods lack.

翻译：共形预测在最小假设下提供有限样本覆盖保证。然而，现有方法将整个建模过程视为黑箱，忽视了利用模块化结构的机会。本文针对两阶段序列模型提出一种共形预测框架，其中上游预测器生成中间表征供下游模型使用。通过将总体预测残差分解为阶段特异性分量，本方法使实践者能够将不确定性归因于特定流水线阶段。我们开发了一种基于族错误率（FWER）控制的风险控制参数选择程序，用于校准阶段缩放参数，并提出适用于非平稳场景的自适应扩展方法以保持长期覆盖保证。在合成分布偏移以及真实供应链和股市数据上的实验表明，本方法在标准共形方法失效的条件下仍能保持覆盖，同时提供可解释的阶段不确定性归因。该框架提供了标准共形方法所不具备的诊断优势和鲁棒覆盖能力。