Estimation and inference on treatment effects under treatment-based sampling designs
Estimation and inference on treatment effects under treatment-based sampling designs
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基于治疗的抽样设计对治疗效果的估计和推断
DOI：
10.1093/ectj/utac008
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yu Zhengfei
中科院分区：
文献类型：
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作者：
Song Kyungchul;Yu Zhengfei
当目标人群中的治疗效果不同于从样本所代表的人群中确定的治疗效果时，项目评估环境中的因果推断面临外部有效性的问题。本文重点讨论基于个体治疗状况和其他特征的分层抽样设计出现这种差异的情况。在这种情况下，设计概率从抽样设计中已知，但目标人群取决于基础人群份额向量，而该向量通常是未知的，并且除特殊情况外，治疗效果参数无法确定。在本文中，我们提出了一种构建对给定人口份额范围有效的置信集的方法。当给出基准群体共享向量和相应的治疗效果参数估计量时，我们开发了一种方法来发现具有家庭误差率控制的外部有效性范围。最后，我们得出一个最佳抽样设计，在给定与目标人口相关的人口份额的情况下，最大限度地减少半参数效率界限。我们提供蒙特卡罗模拟结果和实证应用来证明我们建议的有用性。
Causal inference in a programme evaluation setting faces the problem of external validity when the treatment effect in the target population is different from the treatment effect identified from the population of which the sample is representative. This paper focuses on a situation where such discrepancy arises by a stratified sampling design based on the individual treatment status and other characteristics. In such settings, the design probability is known from the sampling design but the target population depends on the underlying population share vector, which is often unknown, and, except for special cases, the treatment effect parameters are not identified. In this paper we propose a method of constructing confidence sets that are valid for a given range of population shares. When a benchmark population share vector and a corresponding estimator of a treatment effect parameter are given, we develop a method to discover the scope of external validity with familywise error rate control. Finally, we derive an optimal sampling design that minimizes the semiparametric efficiency bound given a population share associated with a target population. We provide Monte Carlo simulation results and an empirical application to demonstrate the usefulness of our proposals.