论文标题
关于许多隐藏混杂因素的近端因果学习
On Proximal Causal Learning with Many Hidden Confounders
论文作者
论文摘要
我们将MIAO，GENG和TCHETGEN TCHETGEN（2018）的近端G形成率概括为使用代理变量未观察到的混杂性下的因果推断。具体而言，我们表明该公式对于某个等价类别中的所有因果模型均为正确，并且该类包含模型，其中一组未观察的混杂因素的总级别可以任意大于每个代理变量的级别的数量。尽管可直接获得，但结果对于应用可能很重要。模拟证实了我们的正式论点。
We generalize the proximal g-formula of Miao, Geng, and Tchetgen Tchetgen (2018) for causal inference under unobserved confounding using proxy variables. Specifically, we show that the formula holds true for all causal models in a certain equivalence class, and this class contains models in which the total number of levels for the set of unobserved confounders can be arbitrarily larger than the number of levels of each proxy variable. Although straightforward to obtain, the result can be significant for applications. Simulations corroborate our formal arguments.
