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 a 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.

翻译：森林类方法近年来在非参数处理效应估计中日益流行。基于这一研究方向，我们提出了因果生存森林，可用于在生存分析和观察性研究场景中估计异质性处理效应，其中结果变量可能存在右删失。我们的方法依赖于正交估计方程，在无混杂假设下对删失效应和选择效应进行稳健调整。实验结果表明，与多种基线方法相比，我们的方法表现出良好的性能。