We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, demand pressure, incremental response, and member-experience load. Instead of calibrating a generic forecast residual, the framework measures forecast error by its largest impact on the policies that could actually be deployed. The main theorem shows that the proposed score is the smallest valid uncertainty measure that uniformly protects all deployable pacing policies. Geometrically, it is the support function of the signed policy sensitivity set. Split conformal calibration gives finite-sample coverage for this score. A high-dimensional separation theorem shows that traditional residual calibration can be arbitrarily more conservative by paying for nuisance inventory dimensions, and a robust pacing result combines inventory, response, and experience uncertainty. On public-data-calibrated pacing replays built from Criteo Uplift and KuaiRand datasets, traditional conformal pacing remains unresolved with high residual radii of 7236.7 on Criteo and 4629.4 on KuaiRand. With the proposed decision calibration approach, the uncertainty radii are reduced to 18.4 and 278.6 respectively, with separate margins for value, delivery, budget, and member load. On Criteo, the proposed method certifies a less aggressive pacing policy than the point-forecast baseline, and reduces held-out any-violation rate from 16.7% to 3.3%, with zero budget and member-load violations. On KuaiRand, the choice remains unresolved. In a nutshell, the paper establishes that forecasts, response estimates, and member-experience models should be judged by whether they shrink the uncertainty that the pacing decision uses, as this leads to confident decisions that are not overly conservative.

翻译：我们提出了一种决策校准的共形框架，用于流式广告中的节奏控制决策。节奏控制依赖于不确定的未来库存、需求压力、增量响应及成员体验负载。该框架并非校准通用的预测残差，而是通过预测误差对实际可部署策略的最大影响来衡量该误差。主要定理表明，所提出的评分是能够统一保护所有可部署节奏控制策略的最小有效不确定度量。从几何角度看，它是符号策略灵敏度集的支撑函数。分割共形校准为此评分提供了有限样本覆盖。高维分离定理表明，传统残差校准可能因支付无关库存维度而任意保守；而一种鲁棒的节奏控制结果结合了库存、响应及体验的不确定性。在基于Criteo Uplift和KuaiRand数据集构建的公开数据校准节奏控制回放中，传统共形节奏控制在Criteo上仍存在高残差半径（7236.7），在KuaiRand上为4629.4。采用所提出的决策校准方法后，不确定度半径分别降至18.4和278.6，并针对价值、交付、预算及成员负载提供了独立边际。在Criteo上，所提出方法认证了一种比点预测基线更不激进的节奏控制策略，并将保留的任意违规率从16.7%降至3.3%，且预算与成员负载违规为零。在KuaiRand上，这一选择仍未解决。简言之，本文确立了以下观点：预测、响应估计及成员体验模型应以其是否缩小节奏控制决策所使用的不确定度为评判标准，因为这能带来既自信又不过度保守的决策。