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However, in practice, many survivor outcomes may remain uncollected or be missing not at random, posing a challenge to standard principal stratification analysis. In this article, we explore the identification, estimation, and bounds of the average treatment effect within a subpopulation of individuals who would potentially survive under both treatment and control conditions. We show that the causal parameter of interest can be identified by introducing a proxy variable that affects the outcome only through the principal strata, while requiring that the treatment variable does not directly affect the missingness mechanism. Subsequently, we propose an approach for estimating causal parameters and derive nonparametric bounds in cases where identification assumptions are violated. We illustrate the performance of the proposed method through simulation studies and a real dataset obtained from a human immunodeficiency virus study.<\/jats:p>","DOI":"10.1002\/sim.70126","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T09:16:04Z","timestamp":1747646164000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Inference With Outcomes Truncated by Death and Missing Not at Random"],"prefix":"10.1002","volume":"44","author":[{"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"Center for Applied Statistics and School of Statistics Renmin University of China  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics and School of Statistics Renmin University of China  Beijing China"},{"name":"Mathematical Institute Leiden 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