处理不完美的随机化:对于高范围的佩里学前计划的推理
James Heckman1, Rodrigo Pinto2, Azeem M Shaikh1
1Department of Economics, University of Chicago.
概括
本研究通过使用部分识别方法来解决程序评估中的不完美随机化问题. 它成功地确定了该计划对多个结果的重大影响,包括减少犯罪活动.
科学领域:
- 计量经济学 计量经济学
- 因果推理因果推理
- 项目评估 项目评估
背景情况:
- 不完美的随机化使程序效果估计变得复杂.
- 根据观察到的/未观察到的特征重新分配治疗状态带来了挑战.
研究的目的:
- 开发一种方法,用不完美的随机化对多个结果做出有效的推断.
- 为了控制程序效应的假设测试中的家族错误率.
主要方法:
- 使用随机化缺陷信息的部分识别方法.
- 开发一种方法来测试一个虚假假设家族.
- 申请HighScope佩里学前教育计划的数据.
主要成果:
- 证明了尽管有不完美的随机化,但仍有可能得出非微不足道的推断.
- 在各种结果上发现了统计学上显著的项目效应.
- 对男性和女性的犯罪活动有所影响.
结论:
- 提出的方法有效地处理不完美的随机化和多个结果.
- 高斯科普佩里幼儿园计划显示了显著的积极影响.
- 即使在不完美的实验设计中,也可以做出强大的统计推断.
关键词:
在C3131中,它是C31.准确的推理 准确的推理实验 实验 实验 实验一个家庭的错误率第121章 没有了不完美的随机化 不完美的随机化J1313 一个人的故事多种结果的多重结果.多重测试 多重测试转换试验 转换试验佩里学前教育计划项目评估 项目评估更多相关视频
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