Instrumented Common Confounding
工具变量化的共同混杂AI 解读 ↓
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工具变量化的共同混杂
- 研究问题
- 存在多种未观测混杂，且工具变量仅在给定部分未观测共同混杂后才外生时，如何非参数识别处理对结果的因果效应？
- 方法与思路
- 作者提出 ICC 框架，利用与共同混杂相关的观测变量，并分别通过结果模型限制或第一阶段限制建立因果效应的点识别。
- 材料陈述的贡献
- 摘要称，该框架允许共同与非共同混杂并存，给出两类点识别路径和假设操作指南，并以教育对收入的效应说明研究动机。
- 阅读关注点
- 应核对共同混杂的定义、相关观测变量须满足的条件，以及两类识别限制与混合模型、负向控制和传统工具变量假设的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实。摘要未说明完整识别条件，不能据此认定一般的条件外生工具均可识别效应。
原文摘要
Causal inference is difficult in the presence of unobserved confounders. We introduce the instrumented common confounding (ICC) approach to (nonparametrically) identify causal effects with instruments, which are exogenous only conditional on some unobserved common confounders. The ICC approach is most useful in rich observational data with multiple sources of unobserved confounding, where instruments are at most exogenous conditional on some unobserved common confounders. Suitable examples of this setting are various identification problems in the social sciences, nonlinear dynamic panels, and problems with multiple endogenous confounders. The ICC identifying assumptions are closely related to those in mixture models, negative control and IV. Compared to mixture models [Bonhomme et al., 2016], we require less conditionally independent variables and do not need to model the unobserved confounder. Compared to negative control [Cui et al., 2020], we allow for non-common confounders, with respect to which the instruments are exogenous. Compared to IV [Newey and Powell, 2003], we allow instruments to be exogenous conditional on some unobserved common confounders, for which a set of relevant observed variables exists. We prove point identification with outcome model and alternatively first stage restrictions. We provide a practical step-by-step guide to the ICC model assumptions and present the causal effect of education on income as a motivating example.