When a long-term outcome is administratively censored for a substantial fraction of a study cohort while a short-term intermediate variable remains broadly available, the target causal parameter can be identified through a nested functional that integrates the outcome regression over the conditional intermediate distribution, avoiding inverse censoring weights entirely. In observational studies where treatment is also confounded, this nested identification creates a semiparametric structure with two distinct positivity boundaries -- one from the censoring mechanism and one from the treatment assignment -- that enter the efficient influence function in fundamentally different roles. The censoring boundary is removed from the identification by the nested functional but remains in the efficient score; the treatment boundary appears in both. We develop the inference theory for this dual-boundary structure. Three results are established.

翻译：当长期结局在研究队列中大部分个体因行政删失而无法观测，而短期中间变量仍广泛可得时，目标因果参数可通过嵌套函数进行识别：该函数将结局回归对条件中间变量分布进行积分，从而完全避免使用逆删失加权。在治疗同样存在混杂的观察性研究中，这种嵌套识别形成了一种具有两个不同正性边界的半参数结构——一个来自删失机制，另一个来自治疗分配——两者在有效影响函数中扮演根本不同的角色。删失边界通过嵌套函数从识别条件中消除，但仍保留在有效评分中；治疗边界则同时出现在两者中。我们针对这种双重边界结构发展了推断理论，并建立了三项主要结果。