Unmeasured confounding, unethical exposure, and ill-defined interventions pose significant challenges to evaluating policy-relevant mediation estimands in medicine and public health. In observational studies involving harmful exposures, the population intervention indirect effect (PIIE) is often more salient than the natural indirect effect, as the latter relies on hypothetical interventions that may be ethically or practically unfeasible. While the PIIE can be identified via the generalized front-door criterion under unmeasured exposure-outcome confounding, existing estimation methods typically assume the absence of unmeasured confounding for the mediator. Furthermore, when the exposure corresponds to ill-defined interventions, the standard PIIE criterion fails; however, the generalized front-door formula may still identify the causal effect of an intervening variable designed to capture the indirect effect. This paper develops a unified identification and estimation framework for the PIIE and the causal effect of an intervening variable in settings with pervasive unmeasured confounding affecting exposure-mediator, exposure-outcome, and mediator-outcome relationships. Specifically, we leverage observed covariates as proxy variables to construct three distinct identification strategies within a proximal causal inference framework. We characterize the semiparametric efficiency bound for the target estimands and develop multiply robust, locally efficient estimators that remain consistent under partial model misspecification. The finite-sample performance of our estimators is demonstrated through simulations. Finally, we apply our methodology to study the indirect effect of alcohol consumption on depression risk as mediated by depersonalization symptoms.

翻译：未测量的混杂因素、不道德暴露及定义不清的干预措施，给评估医学与公共卫生领域与政策相关的中介效应量带来重大挑战。在涉及有害暴露的观察性研究中，群体干预间接效应通常比自然间接效应更具现实意义，因为后者依赖于可能在伦理或实践中不可行的假设性干预。尽管在未测量暴露-结果混杂因素的情况下，群体干预间接效应可通过广义前门准则识别，但现有估计方法通常假设中介变量不存在未测量混杂因素。此外，当暴露对应于定义不清的干预措施时，标准群体干预间接效应准则失效，但广义前门公式仍可识别旨在捕捉间接效应的中介变量的因果效应。本文针对暴露-中介、暴露-结果及中介-结果关系中存在普遍未测量混杂因素的情境，发展了群体干预间接效应及中介变量因果效应的统一识别与估计框架。具体而言，我们利用观测协变量作为代理变量，在近端因果推断框架内构建三种不同的识别策略。我们刻画了目标估计量的半参数效率界，并开发了多重稳健且局部有效的估计量，该估计量在模型部分误设时仍能保持一致性。通过模拟验证了估计量的有限样本性能。最后，我们将该方法应用于研究酒精消费通过人格解体症状对抑郁风险的中介间接效应。