论文标题
通过因果生存森林估计使用右核心数据的异质治疗效果
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
论文作者
论文摘要
基于森林的方法最近在非参数治疗效应估计中获得了知名度。在这一工作的基础上，我们引入了因果生存森林，可用于在可能右审查结果的生存和观察环境中估计异质治疗效果。我们的方法依赖于正交估计方程来在不满意的情况下对审查和选择效果进行鲁棒性调整。在我们的实验中，我们发现相对于许多基线的表现良好的方法。
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.
