MULTIPLE TESTING AND HETEROGENEOUS TREATMENT EFFECTS: RE‐EVALUATING THE EFFECT OF PROGRESA ON SCHOOL ENROLLMENT
多重检验与异质性处理效应：重新评估 PROGRESA 对入学率的影响AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕政策处理效应的群体异质性及多重检验展开，直接对应因果推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
多重检验与异质性处理效应：重新评估 PROGRESA 对入学率的影响
- 研究问题
- 项目效果随可观测特征变化时，如何判断哪些子群体受到影响，并控制同时检验多个子群体造成的错误拒绝风险？
- 方法与思路
- 作者将子群体效果识别转化为一组假设检验，以性别和最高已完成年级划分群体，构造控制家族错误率的检验程序。
- 材料陈述的贡献
- 摘要称程序在弱假设下有限样本控制家族错误率；PROGRESA 仍影响入学率，但显著子群体比未校正多重检验时少得多。
- 阅读关注点
- 核对所谓弱假设的具体内容、子群体划分和检验构造，并比较校正前后的拒绝结果、检验功效与政策解释。
- 阅读边界
- 仅据所给 Crossref 摘要，无法确定具体识别条件、显著子群体名单及效果大小；未显著也不能直接解释为没有效果。
原文摘要
SUMMARY The effect of a program or treatment may vary according to observed characteristics. In such a setting, it may not only be of interest to determine whether the program or treatment has an effect on some sub‐population defined by these observed characteristics, but also to determine for which sub‐populations, if any, there is an effect. This paper treats this problem as a multiple testing problem in which each null hypothesis in the family of null hypotheses specifies whether the program has an effect on the outcome of interest for a particular sub‐population. We develop our methodology in the context of PROGRESA, a large‐scale poverty‐reduction program in Mexico. In our application, the outcome of interest is the school enrollment rate and the sub‐populations are defined by gender and highest grade completed. Under weak assumptions, the testing procedure we construct controls the familywise error rate—the probability of even one false rejection—in finite samples. Similar to earlier studies, we find that the program has a significant effect on the school enrollment rate, but only for a much smaller number of sub‐populations when compared to results that do not adjust for multiple testing. Copyright © 2013 John Wiley & Sons, Ltd.