Skepticism about the assumption of no unmeasured confounding, also known as exchangeability, is often warranted in making causal inferences from observational data; because exchangeability hinges on an investigator's ability to accurately measure covariates that capture all potential sources of confounding. In practice, the most one can hope for is that covariate measurements are at best proxies of the true underlying confounding mechanism operating in a given observational study. In this paper, we consider the framework of proximal causal inference introduced by Miao et al. (2018); Tchetgen Tchetgen et al. (2020), which while explicitly acknowledging covariate measurements as imperfect proxies of confounding mechanisms, offers an opportunity to learn about causal effects in settings where exchangeability on the basis of measured covariates fails. We make a number of contributions to proximal inference including (i) an alternative set of conditions for nonparametric proximal identification of the average treatment effect; (ii) general semiparametric theory for proximal estimation of the average treatment effect including efficiency bounds for key semiparametric models of interest; (iii) a characterization of proximal doubly robust and locally efficient estimators of the average treatment effect. Moreover, we provide analogous identification and efficiency results for the average treatment effect on the treated. Our approach is illustrated via simulation studies and a data application on evaluating the effectiveness of right heart catheterization in the intensive care unit of critically ill patients.

翻译：关于无未测量混杂假设（也称可交换性）的怀疑在从观察性数据进行因果推断时往往具有合理性；这是因为可交换性依赖于研究者能否准确测量协变量以捕获所有可能的混杂来源。在实践中，我们至多能期望协变量测量仅是驱动特定观察性研究的潜在混杂机制的不完美代理变量。本文考虑由Miao等（2018）和Tchetgen Tchetgen等（2020）提出的近端因果推断框架，该框架在明确承认协变量测量是混杂机制不完美代理变量的同时，为当基于测量协变量的可交换性不成立时学习因果效应提供了机会。我们在近端推断方面做出多项贡献，包括：（i）平均处理效应的非参数近端识别替代条件集；（ii）平均处理效应的近端估计一般半参数理论，包含关键半参数模型的效率界；（iii）平均处理效应的近端双稳健与局部有效估计表征。此外，我们提供了处理组的平均处理效应的类比识别与效率结果。通过模拟研究和一项关于评价重症监护病房危重患者右心导管置入术有效性的数据应用，阐述了我们的方法。