Estimating heterogeneous treatment effects with right-censored data via causal survival forests
提出因果生存森林方法，用于在生存分析和观察性研究中估计异质性处理效应，能处理结果变量右删失的情况，并通过正交估计方程稳健调整删失和选择效应。
Abstract 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.