Variable importance measures for heterogeneous treatment effects with survival outcome
生存结局下异质性处理效应的变量重要性度量AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究生存结局中的条件平均处理效应、异质性及其变量重要性。
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生存结局下异质性处理效应的变量重要性度量
- 研究问题
- 面对有删失的生存结局，如何识别哪些协变量驱动条件平均处理效应的异质性，并提供超出效应预测本身的可解释重要性度量？
- 方法与思路
- 作者将处理效应变量重要性度量扩展到基于生存函数和限制平均生存时间的两类效应，并提出基于最佳部分线性投影的新度量及估计量。
- 材料陈述的贡献
- 摘要称通过半参数效率理论构造估计量，给出渐近线性的条件，并以模拟及两个真实数据示例比较所提出的异质性度量。
- 阅读关注点
- 应核对两类条件处理效应的时间尺度、删失与因果识别条件、变量重要性的定义，以及投影参数和渐近线性所需的估计误差条件。
- 阅读边界
- 这是arXiv预印本，不能据此认定已同行评审或期刊接收。摘要未列出删失与识别条件，条件平均效应也不足以识别个体处理效应。
原文摘要
Treatment effect heterogeneity plays an important role in many areas of causal inference and within recent years, estimation of the conditional average treatment effect (CATE) has received much attention in the statistical community. While accurate estimation of the CATE-function through flexible machine learning procedures provides a tool for prediction of the individual treatment effect, it does not provide further insight into the driving features of potential treatment effect heterogeneity. Recent papers have addressed this problem by providing variable importance measures for treatment effect heterogeneity. Most of the suggestions have been developed for continuous or binary outcome, while little attention has been given to censored time-to-event outcome. In this paper, we extend the treatment effect variable importance measure (TE-VIM) proposed in Hines et al. (2022) to the survival setting with censored outcome. We derive an estimator for the TE-VIM for two different CATE functions based on the survival function and RMST, respectively. Along with the TE-VIM, we propose a new measure of treatment effect heterogeneity based on the best partially linear projection of the CATE and suggest accompanying estimators for that projection. All estimators are based on semiparametric efficiency theory, and we give conditions under which they are asymptotically linear. The finite sample performance of the derived estimators are investigated in a simulation study. Finally, the estimators are applied and contrasted in two real data examples.