Monotone Instrumental Variables: With an Application to the Returns to Schooling
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AI 根据所提供的论文材料归类：摘要直接研究处理效应的识别、工具变量与单调性假设，并以教育回报的界限为应用，因此归入因果推断。
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单调工具变量：教育回报的应用
- 研究问题
- 当传统工具变量假设的可信度存在争议时，较弱的单调性限制能在多大程度上识别处理效应，并约束教育回报？
- 方法与思路
- 作者提出单调工具变量（MIV）及其特例单调处理选择（MTS），研究它们单独和与单调处理响应（MTR）结合时的识别能力。
- 材料陈述的贡献
- 摘要称作者提出 MIV 与 MTS，分析这些单调性假设对处理响应的识别能力，并在教育回报应用中结合 MTS 与 MTR 给出上界。
- 阅读关注点
- 核对 MIV、MTS 与 MTR 的数学定义及经济含义、单独与联合假设下的界限推导，以及教育回报上界对各假设的依赖。
- 阅读边界
- 给定摘要混有导言和文献讨论，未交代教育回报上界的数值、推断程序及单调性假设的经验支持，不能据此评估应用的稳健性。
原文摘要
Econometric analyses of treatment response commonly use instrumental variable (IV) assumptions to identify treatment effects.Yet the credibility of IV assumptions is often a matter of considerable disagreement.There is therefore good reason to consider weaker but more credible assumptions.To this end, we introduce monotone instrumental variable (MIV) assumptions and the important special case of monotone treatment selection (MTS).We study the identifying power of MIV assumptions alone and combined with the assumption of monotone treatment response (MTR).We present an empirical application using the MTS and MTR assumptions to place upper bounds on the returns to schooling.1 Whereas a variable v was originally called an instrumental variable if v has zero covariance with a residual ,, the modern usage of the term has broadened to embrace assumptions that specified functions of v and , are orthogonal.Hence it is now necessary to specify the type of IV assumption one has in mind.Mean independence, quantile independence, and statistical independence assumptions (or the orthogonality conditions that these assumptions yield) have all been prominent in the literature.See Manski (1988) pp.25-26 and Section 6.1 for discussion of the history and exposition of the variety of modern IV assumptions.2 Several contributors to this literature have examined the identifying power of IV assumptions in the absence of parametric restrictions on the form of response functions.Manski (1990Manski ( , 1994) ) showed that mean independence assumptions imply sharp bounds on mean outcomes and average treatment effects.Robins (1989) and Balke and Pearl (1997) have considered the statistical independence assumption that holds in classical randomized experiments, where response functions are statistically independent of assigned treatments.Hotz, Mullins, and Sanders (1997) have studied contaminated instrument assumptions.These suppose that a mean-independence assumption holds in a population of interest, but the observed population is a probability mixture of the population of interest and one in which the assumption does not hold.