Marginal and Nested Structural Models Using Instrumental Variables
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AI 根据所提供的论文材料归类：摘要直接研究工具变量、潜在结果、治疗效应与双重稳健估计，属于因果推断。
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使用工具变量的边际与嵌套结构模型
- 研究问题
- 存在未观测混杂时，如何利用工具变量刻画不同工具水平下会接受治疗的协变量特定人群的潜在结果及治疗效应？
- 方法与思路
- 作者提出边际和嵌套结构工具变量模型，分别参数化潜在结果均值与链接函数变换后的均值差，并构造回归、加权和双重稳健估计。
- 材料陈述的贡献
- 摘要称双重稳健估计在两组模型任一组正确设定时保持一致，并用青年男性纵向调查数据研究教育回报。
- 阅读关注点
- 应核对工具变量条件、各工具水平对应的目标人群、链接函数对效应解释的影响，以及双重稳健性所对应的两组模型。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；工具变量在教育应用中的有效性、完整识别条件和有限样本表现不能由摘要验证。
原文摘要
The objective of many scientific studies is to evaluate the effect of a treatment on an outcome of interest ceteris paribus. Instrumental variables (IVs) serve as an experimental handle, independent of potential outcomes and potential treatment status and affecting potential outcomes only through potential treatment status. We propose marginal and nested structural models using IVs, in the spirit of marginal and nested structural models under no unmeasured confounding. A marginal structural IV model parameterizes the expectations of two potential outcomes under an active treatment and the null treatment respectively, for those in a covariate-specific subpopulation who would take the active treatment if the instrument were externally set to each specific level. A nested structural IV model parameterizes the difference between the two expectations after transformed by a link function and hence the average treatment effect on the treated at each instrument level. We develop IV outcome regression, IV propensity score weighting, and doubly robust methods for estimation, in parallel to those for structural models under no unmeasured confounding. The regression method requires correctly specified models for the treatment propensity score and the outcome regression function. The weighting method requires a correctly specified model for the instrument propensity score. The doubly robust estimators depend on the two sets of models and remain consistent if either set of models are correctly specified. We apply our methods to study returns to education using data from the National Longitudinal Survey of Young Men.