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分类依据与纠错

AI 根据所提供的论文材料归类:摘要直接研究工具变量因果分析、非随机鼓励分配和未观测偏差敏感性。

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关注这篇论文的作者
Dylan S Small
Paul R Rosenbaum

战争与工资

研究问题
研究工具变量分析对鼓励分配偏离随机性的敏感程度,尤其考察工具强度如何影响二战服役与后续收入效应的可信度。
方法与思路
作者以设计敏感度研究鼓励分配中的潜在偏差,并通过选择不同出生队列构造强度不同的工具,重新分析服役收入关系。
材料陈述的贡献
摘要称工具变量分析使朴素工资比较的符号反转,并指出弱工具即使样本很大也可能对微小偏差极敏感,强工具则可能更稳健。
阅读关注点
应核对设计敏感度的定义、偏差模型和工具强度度量,尤其检查不同出生队列是否确实具有可比较的潜在偏差。
阅读边界
索引未注明稿件版本;摘要未给出敏感度阈值、完整条件或数值结果,不能据此认定工具有效或服役效应已被确定。

原文摘要

An instrument manipulates a treatment that it does not entirely control, but the instrument affects the outcome only indirectly through its manipulation of the treatment. The idealized prototype is the randomized encouragement design, in which subjects are randomly assigned to receive either encouragement to accept the treatment or no such encouragement, but not all subjects comply by doing what they are encouraged to do, and the situation is such that only the treatment itself, not disregarded encouragement alone, can affect the outcome. An instrument is weak if it has only a slight impact on acceptance of the treatment, that is, if most people disregard encouragement to accept the treatment. Typical applications of instrumental variables are not ideal; encouragement is not randomized, although it may be assigned in a far less biased manner than the treatment itself. Using the concept of design sensitivity, we study the sensitivity of instrumental variable analyses to departures from the ideal of random assignment of encouragement, with particular reference to the strength of the instrument. With these issues in mind, we reanalyze a clever study by Angrist and Krueger concerning the effects of military service during World War II on subsequent earnings, in which birth cohorts of very similar but not identical age were differently “encouraged” to serve in the war. A striking feature of this example is that those who served earned more, but the effect of service on earnings appears to be negative; that is, the instrumental variables analysis reverses the sign of the naive comparison. For expository purposes, this example has the convenient feature of enabling, by selecting different birth cohorts, the creation of instruments of varied strength, from extremely weak to fairly strong, although separated by the same time interval and thus perhaps similarly biased. No matter how large the sample size becomes, even if the effect under study is quite large, studies with weak instruments are extremely sensitive to tiny biases, whereas studies with stronger instruments can be insensitive to moderate biases.

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