Estimating heterogeneous treatment effects with right-censored data via causal survival forests

• 成果类型: Article
• 署名作者: Cui, Yifan; Kosorok, Michael R.; Sverdrup, Erik; Wager, Stefan; Zhu, Ruoqing
• 署名单位: Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Stanford University; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkac001
• 发表日期: 2023
• 页码: 179-211
• 关键词: estimating individualized treatment; randomized clinical-trials; treatment rules; structural-change; regression; TREE; Consistency; inference; models; tests

摘要

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.