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.

翻译：在未观察到的困惑者在场的情况下,很难得出因果关系的推断。我们引入了工具式的共同混淆(ICC)方法,以(非对称地)确定工具的因果关系,因为工具是外源的,只是一些未观察到的共同困惑者才具有外部。国际商会的方法在丰富的观测数据中最为有用,有多种未观察到的混乱来源,工具大多数是外源的,条件是一些未观察到的共同困惑者。这种背景的恰当例子是社会科学中的各种识别问题、非线性动态面板,以及多种内生共产体的问题。国际商会的识别假设与混合模型、负控制和四类工具的假设密切相关。与混合模型[Bonhommus等人,2016年]相比,我们要求不那么有条件的独立变量,而不需要模拟未观察到的混淆者。与消极控制相比,[Cui 等人,2020年],我们允许非共生共知者,与工具的外源相关。与四[Newey和鲍威尔,2003年]相比,我们允许工具的识别假设与混合模型密切相关,我们所观察到的因果关系是一些共同的变量。