论文标题
控制潜在混杂与三重代理
Controlling for Latent Confounding with Triple Proxies
论文作者
论文摘要
我们提出了新的结果，用于使用嘈杂的代理对未观察到的混杂因素进行非参数鉴定。我们的方法基于\ citet {Hu2008}的结果，后者解决了一般测量误差问题。我们将其称为“三重代理”方法，因为它需要三个在不可观察的情况下共同独立的代理。我们考虑第三个代理的三种不同选择：这可能是结果，处理的矢量或辅助变量的集合。我们与\ citet {miao2018a}引入的替代识别策略进行比较，其中使用两个有条件独立的代理鉴定了因果效应。我们将其称为“双重代理”方法。三重代理方法标识了双重代理方法未识别的对象，其中包括一些捕获不可观察到的层之间平均治疗效应的变化。此外，双重代理方法中的条件独立性假设是非巢穴的。
We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who tackle the problem of general measurement error. We call this the `triple proxy' approach because it requires three proxies that are jointly independent conditional on unobservables. We consider three different choices for the third proxy: it may be an outcome, a vector of treatments, or a collection of auxiliary variables. We compare to an alternative identification strategy introduced by \citet{Miao2018a} in which causal effects are identified using two conditionally independent proxies. We refer to this as the `double proxy' approach. The triple proxy approach identifies objects that are not identified by the double proxy approach, including some that capture the variation in average treatment effects between strata of the unobservables. Moreover, the conditional independence assumptions in the double and triple proxy approaches are non-nested.
