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 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：关于假定不存在无法测量的混杂因素,也称为互换性,这种假设的怀疑性常被证明是为了从观察数据中作出因果关系推断;因为互换性取决于调查员准确测量能捕捉所有潜在汇兑来源的共变因素的能力。在实践中,最人们所希望的是,共变测量结果充其量只是某一观测研究中运行的真正基本汇兑机制的替代物。在本文中,我们考虑了Tchetgen Tchetgen等人(202020年)提出的先验性密集因果推断框架,其中明确承认共变测量数据是混杂机制的不完善的替代物,同时也为了解根据经测量的共变差因素失效的可交换因素中因果影响提供了机会。我们作出了一些贡献,以推导出准确的推导力,包括:(一) 一套用于对平均治疗效果进行非对准性准性准性准性准性辨别的方法(二) 用于估计平均治疗效果的一般半偏重度理论,包括关键半偏偏偏心处理型模型的效率边框。 (三) 当地测测测测测测测测测平均处理效率的结果。