Semiparametric Inference under Dual Positivity Boundaries:Nested Identification with Administrative Censoring and Confounded Treatment

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.

💡 Research Summary

The paper tackles a common but challenging situation in observational longitudinal studies: a long‑term outcome Y is administratively censored for a substantial proportion of participants, while a short‑term intermediate variable S is observed for almost everyone. Under the “surrogate‑mediated missing‑at‑random” assumption (Δ ⟂ Y | S, A, W), the authors show that the average treatment effect can be identified by a nested functional that integrates the outcome regression (\bar Q_Y(S,a,W)=E