Treatment effect quantiles in stratified randomized experiments and matched observational studies
分层随机实验与匹配观察研究中的个体处理效应分位数AI 解读 ↓
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分层随机实验与匹配观察研究中的个体处理效应分位数
- 研究问题
- 当个体处理效应存在重尾或极端值时，如何对其分位数进行可靠推断，并评估匹配观察研究中未测混杂的影响？
- 方法与思路
- 作者采用随机化推断，将分位数假设检验的有效p值计算转化为多选背包问题，再扩展至匹配研究的敏感性分析。
- 材料陈述的贡献
- 摘要称，该方法可精确或略保守地计算有效p值，且随机化推断与敏感性分析对全部个体效应分位数同时有效。
- 阅读关注点
- 应核对个体效应分位数与结果分布分位数之差的区别、分层随机化机制、同时有效性的含义及敏感性参数设定。
- 阅读边界
- 仅依据所给Crossref摘要；未说明检验构造、计算复杂度及混杂模型细节，不能将个体效应分位数视为已被点识别。
原文摘要
Summary Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatment effects, which can be more robust in the presence of extreme individual effects. Moreover, our inference for them is purely randomization based, avoiding any distributional assumptions on the units. We first consider inference in stratified randomized experiments, extending the recent work by Caughey et al. (2021). We show that the computation of valid p-values for testing null hypotheses on quantiles of individual effects can be transformed into instances of the multiple-choice knapsack problem, which can be efficiently solved exactly or slightly conservatively. We then extend our approach to matched observational studies and propose a sensitivity analysis to investigate to what extent our inference on quantiles of individual effects is robust to unmeasured confounding. The proposed randomization inference and sensitivity analysis are simul- taneously valid for all quantiles of individual effects, noting that the analysis for the maximum or minimum individual effect coincides with the conventional analysis assuming constant treatment effects.