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The key idea of the proposed method is to first learn a conditional generator for the joint conditional distribution of the observed time and censoring indicator given covariates, and then construct the Kaplan\u2013Meier and Nelson\u2013Aalen estimators based on this conditional generator for conditional hazard and survival functions. Our method combines ideas from the recently developed deep generative learning and classical nonparametric estimation in survival analysis. We establish the convergence properties of the generative nonparametric estimators. Our numerical studies validate the proposed method.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag164","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T11:20:59Z","timestamp":1789384859000},"source":"Crossref","is-referenced-by-count":0,"title":["Generative estimation of conditional survival function"],"prefix":"10.1093","volume":"82","author":[{"given":"Xingyu","family":"Zhou","sequence":"first","affiliation":[{"name":"Enterprise Innovation and Growth, Dow Inc. , Lake Jackson, 77566 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4597-4541","authenticated-orcid":false,"given":"Wen","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, City University of Hong Kong ,","place":["Hong Kong"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Applied 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