Delayed primary outcomes and administratively censored follow-up create a general semiparametric estimation problem: the target causal functional depends on an endpoint observed only for a shrinking subset of units at analysis time, while earlier surrogate measurements remain widely available. In such settings, inverse-probabilityweighted estimators can become unstable as observation probabilities approach the positivity boundary, and complete-case model-based analyses can be highly sensitive to outcome-model specification. We develop a surrogate-assisted targeted minimum loss estimator for this nested causal functional. Identification proceeds through a surrogate-bridge representation that integrates an observed-outcome regression over the conditional surrogate distribution, thereby avoiding inverse observation weights in the target parameter itself. We show that the estimator is asymptotically linear and doubly robust (in the sense that first-order bias vanishes when either nuisance component is consistently estimated), and we characterize two structural features of the problem: under surrogate-mediated missing at random, the censoring mechanism contributes no separate tangent-space component to the efficient influence function; and for nested bridge functionals, a one-step debiased machine-learning construction leaves a second-order cross-product remainder involving the conditional surrogate law. The proposed two-stage targeting step removes this term without requiring direct estimation of that law. Simulation studies demonstrate stable finite-sample performance under substantial administrative censoring, and a design-calibrated analysis based on the Washington State EPT study illustrates the method in a realistic stepped-wedge cluster-randomized setting.

翻译：延迟的主要结局和行政删失的随访构成了一个广义的半参数估计问题：目标因果函数依赖于仅在分析时对逐渐缩小的个体子集观察到的终点，而较早的替代测量值仍广泛可得。在此类设定中，逆概率加权估计器在观测概率趋近于正性边界时可能变得不稳定，而基于完全案例模型的分析可能对结局模型设定高度敏感。我们为此嵌套因果函数开发了一种替代辅助目标最小损失估计器。识别过程通过替代桥接表示实现，该表示将观测结局回归整合到条件替代分布上，从而避免了目标参数本身中的逆观测权重。我们证明该估计器是渐近线性且双重稳健的（即当任一干扰成分被一致估计时一阶偏差消失），并刻画了该问题的两个结构特征：在替代介导的随机缺失下，删失机制对有效影响函数不贡献独立的切空间分量；对于嵌套桥接函数，一步去偏机器学习构造会留下涉及条件替代分布律的二阶交叉乘积余项。所提出的两阶段目标化步骤无需直接估计该分布律即可消除此项。模拟研究证明了在严重行政删失下稳定的有限样本性能，基于华盛顿州EPT研究的设计校准分析展示了该方法在现实阶梯式楔形整群随机化设定中的应用。