论文标题
仪器共同的混杂
Instrumented Common Confounding
论文作者
论文摘要
在没有观察到的混杂因素的情况下，因果推断很难。我们将仪器的共同混杂（ICC）方法引入（非参数）与仪器的因果效应，这些仪器仅在某些未观察到的普通混杂因子上有条件。 ICC方法在具有多种未观察到的混杂源的丰富观察数据中最有用，在某些未观察到的共同混杂因子上，仪器最多是外源的。这种设置的适当示例是社会科学，非线性动态面板中的各种识别问题，以及多个内源性混杂因素的问题。 ICC识别假设与混合模型，阴性对照和IV中的假设密切相关。与混合模型相比[Bonhomme等，2016]，我们需要的有条件自变量较少，并且不需要对未观察到的混杂因素进行建模。与阴性对照[Cui等人，2020年]相比，我们允许使用仪器是外源性的非共同混杂因素。与IV [Newey and Powell，2003年]相比，我们允许仪器在某些未观察到的共同混杂因子上是外源的条件，其中一组相关的观察到的变量存在。我们通过结果模型证明了点识别，并进行了第一阶段的限制。我们为ICC模型假设提供了实用的逐步指南，并提出了教育对收入的因果影响，这是一个激励人心的例子。
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.
