{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T00:06:06Z","timestamp":1792886766340,"version":"4.5.0"},"reference-count":82,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T00:00:00Z","timestamp":1677456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Science Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation. Building on this line of work, we introduce causal survival forests, which can be used to estimate heterogeneous treatment effects in survival and observational setting where outcomes may be right-censored. Our approach relies on orthogonal estimating equations to robustly adjust for both censoring and selection effects under unconfoundedness. In our experiments, we find our approach to perform well relative to a number of baselines.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkac001","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T07:21:44Z","timestamp":1677482504000},"page":"179-211","source":"Crossref","is-referenced-by-count":107,"title":["Estimating heterogeneous treatment effects with right-censored data via causal survival forests"],"prefix":"10.1093","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9957-7955","authenticated-orcid":false,"given":"Yifan","family":"Cui","sequence":"first","affiliation":[{"name":"Center for Data Science, Zhejiang University , Hangzhou , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael R","family":"Kosorok","sequence":"additional","affiliation":[{"name":"Departments of Biostatistics and Statistics & Operations Research, University of North Carolina at Chapel Hill , Chapel Hill , 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