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AI 根据所提供的论文材料归类:摘要在随机评估中研究处理效应的非参数界,同时明确涉及分位数效应与异质性,直接对应因果推断及分布效应。

反馈分类问题

Bounds on Average and Quantile Treatment Effects on Duration Outcomes Under Censoring, Selection, and Noncompliance

删失、选择与不依从下持续时间结果的平均与分位数处理效应界

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代码、数据与补充材料

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关注这篇论文的作者
German Blanco
Xuan Chen
Carlos A. Flores

删失、选择与不依从下持续时间结果的平均与分位数处理效应界

研究问题
在存在处理不依从的随机评估中,如何同时应对进入目标持续期的自选择与内生删失,并界定平均及分位数处理效应?
方法与思路
作者以单调性和随机占优假设推导非参数尖锐界,不要求独立删失,也不以处理删失和选择内生性的排除限制为前提。
材料陈述的贡献
摘要称所得界同时处理上述三类问题且为尖锐界;Job Corps 应用提示特定依从者群体的就业持续时间可能增加。
阅读关注点
应核对单调性与随机占优施加于哪些变量、尖锐界的构造及适用人群,并检查 Job Corps 就业期与观察窗口的定义。
阅读边界
效应界依赖单调性和随机占优,具体形式在摘要中未展开;项目结果适用于特定人群与观察窗口,不能直接作总体点效应解释。

原文摘要

We consider the problem of assessing the effects of a treatment on duration outcomes using data from a randomized evaluation with noncompliance. For such settings, we derive nonparametric sharp bounds for average and quantile treatment effects addressing three pervasive problems simultaneously: self-selection into the spell of interest, endogenous censoring of the duration outcome, and noncompliance with the assigned treatment. Ignoring any of these issues could yield biased estimates of the effects. Notably, the proposed bounds do not impose the independent censoring assumption—which is commonly used to address censoring but is likely to fail in important settings—or exclusion restrictions to address endogeneity of censoring and selection. Instead, they employ monotonicity and stochastic dominance assumptions. To illustrate the use of these bounds we assess the effects of the Job Corps (JC) training program on its participants’ last complete employment spell duration. Our estimated bounds suggest that JC participation may increase the average duration of the last complete employment spell before week 208 after randomization by at least 5.6 log points (5.8%) for individuals who comply with their treatment assignment and experience a complete employment spell whether or not they enrolled in JC. The estimated quantile treatment effects suggest the impacts may be heterogeneous, and strengthen our conclusions based on the estimated average effects.

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