标题： 带有许多无效代理的强化邻近因果推断 显示英文标题

标题： Fortified Proximal Causal Inference with Many Invalid Proxies

摘要： 从观察数据进行因果推断通常依赖于无未测量混杂因素的假设，而这一假设在实践中经常由于未观察到或测量不准确的协变量而被违反。 近端因果推断 (PCI) 提供了一个有前景的框架，可以使用一对结果和治疗混杂代理变量来解决未测量的混杂因素。 然而，现有的 PCI 方法通常假设所有指定的代理变量都是有效的，这可能不切实际，并且如果没有额外的假设就无法检验。 在本文中，我们开发了一种用于多代理 PCI 设置的半参数方法，该方法可以适应可能无效的治疗混杂代理变量。 我们引入了一类新的强化混杂桥接函数，并建立了对总体平均治疗效应 (ATE) 的非参数识别，假设对于分析人员设定的任何 $\gamma \leq K$，至少 $\gamma$ 个候选治疗混杂代理变量有效，而无需知道哪些代理变量有效。 我们建立了局部半参数效率界，并开发了一类多重稳健、局部有效的 ATE 估计器。 因此，这些估计器同时对无效治疗混杂代理变量和干扰参数的模型错误设定具有稳健性。 我们通过仿真对所提出的方法进行了评估，并将其应用于评估重症患者右心导管插入术的效果。 显示英文摘要

摘要： Causal inference from observational data often relies on the assumption of no unmeasured confounding, an assumption frequently violated in practice due to unobserved or poorly measured covariates. Proximal causal inference (PCI) offers a promising framework for addressing unmeasured confounding using a pair of outcome and treatment confounding proxies. However, existing PCI methods typically assume all specified proxies are valid, which may be unrealistic and is untestable without extra assumptions. In this paper, we develop a semiparametric approach for a many-proxy PCI setting that accommodates potentially invalid treatment confounding proxies. We introduce a new class of fortified confounding bridge functions and establish nonparametric identification of the population average treatment effect (ATE) under the assumption that at least $\gamma$ out of $K$ candidate treatment confounding proxies are valid, for any $\gamma \leq K$ set by the analyst without requiring knowledge of which proxies are valid. We establish a local semiparametric efficiency bound and develop a class of multiply robust, locally efficient estimators for the ATE. These estimators are thus simultaneously robust to invalid treatment confounding proxies and model misspecification of nuisance parameters. The proposed methods are evaluated through simulation and applied to assess the effect of right heart catheterization in critically ill patients.