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：当长期结局在队列中大部分受试者因行政删失而不可观测，而短期中间变量广泛可得时，目标因果参数可通过嵌套泛函进行识别——该泛函将结局回归在条件中间分布上积分，从而完全避免使用逆删失权重。在存在处理混杂的观察性研究中，这种嵌套识别产生了具有两类不同阳性边界的半参数结构，一类来自删失机制，另一类来自处理分配，它们在有效影响函数中扮演着截然不同的角色。删失边界虽被嵌套泛函从识别中移除，但仍保留在有效得分中；处理边界则同时出现在两者中。我们针对这种双边界结构发展了推断理论。建立了三项主要结果。