Non-Parametric Tests for Distributional Treatment Effect for Randomly Censored Responses
随机删失结果的分布处理效应非参数检验AI 解读 ↓
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随机删失结果的分布处理效应非参数检验
- 研究问题
- 处理组与对照组的协变量分布不同且结果受到随机删失时，如何检验给定协变量后两种潜在结果的条件分布是否相同？
- 方法与思路
- 作者提出协变量匹配的非参数检验，借鉴 Gehan 的思路利用删失观测，并比较跨处理组和对照组的所有可用配对。
- 材料陈述的贡献
- 摘要称该检验可用于随机删失的观察数据，具有针对协变量支持不重叠问题的内置保护，并提供模拟及培训对失业时长的应用。
- 阅读关注点
- 应核对观察研究中的因果识别与删失条件、协变量匹配权重，以及支持不重叠保护具体针对的检验对象和有效性范围。
- 阅读边界
- 仅依据 Crossref 摘要。摘要未说明识别假设、删失独立条件或支持保护的具体机制，不能据此认定其解决任意混杂与重叠不足问题。
原文摘要
Summary For a binary treatment ν = 0, 1 and the corresponding ‘potential response’Y0 for the control group (ν = 0) and Y1 for the treatment group (ν = 1), one definition of no treatment effect is that Y0 and Y1 follow the same distribution given a covariate vector X. Koul and Schick have provided a non-parametric test for no distributional effect when the realized response (1−ν)Y0+νY1 is fully observed and the distribution of X is the same across the two groups. This test is thus not applicable to censored responses, nor to non-experimental (i.e. observational) studies that entail different distributions of X across the two groups. We propose ‘X-matched’ non-parametric tests generalizing the test of Koul and Schick following an idea of Gehan. Our tests are applicable to non-experimental data with randomly censored responses. In addition to these motivations, the tests have several advantages. First, they have the intuitive appeal of comparing all available pairs across the treatment and control groups, instead of selecting a number of matched controls (or treated) in the usual pair or multiple matching. Second, whereas most matching estimators or tests have a non-overlapping support (of X) problem across the two groups, our tests have a built-in protection against the problem. Third, Gehan’s idea allows the tests to make good use of censored observations. A simulation study is conducted, and an empirical illustration for a job training effect on the duration of unemployment is provided.