Proximal causal inference (PCI) has emerged as a promising framework for identifying and estimating causal effects in the presence of unobserved confounders. While many traditional causal inference methods rely on the assumption of no unobserved confounding, this assumption is likely often violated. PCI mitigates this challenge by relying on an alternative set of assumptions regarding the relationships between treatment, outcome, and auxiliary variables that serve as proxies for unmeasured confounders. We review existing identification results, discuss the assumptions necessary for valid causal effect estimation via PCI, and compare different PCI estimation methods. We offer practical guidance on operationalizing PCI, with a focus on selecting and evaluating proxy variables using domain knowledge, measurement error perspectives, and negative control analogies. Through conceptual examples, we demonstrate tensions in proxy selection and discuss the importance of clearly defining the unobserved confounding mechanism. By bridging formal results with applied considerations, this work aims to demystify PCI, encourage thoughtful use in practice, and identify open directions for methodological development and empirical research.

翻译：近端因果推断（PCI）作为一种有前景的框架，在存在未观测混杂因子的情况下，为因果效应的识别与估计提供了新途径。尽管许多传统因果推断方法依赖于无未观测混杂的假设，但这一假设在实际中常被违背。PCI通过依赖另一组关于处理变量、结果变量及辅助变量之间关系的假设来应对这一挑战，这些辅助变量可作为未测量混杂因子的代理变量。本文回顾了现有的识别结果，讨论了通过PCI进行有效因果效应估计所需的假设，并比较了不同的PCI估计方法。我们提供了实施PCI的实用指南，重点在于如何利用领域知识、测量误差视角及阴性控制类比来选择和评估代理变量。通过概念性示例，我们展示了代理变量选择中的权衡关系，并讨论了明确定义未观测混杂机制的重要性。通过将形式化结果与应用考量相结合，本文旨在解析PCI的核心原理，促进其在实践中的审慎应用，并指出方法论发展与实证研究的开放方向。