Although the exposure can be randomly assigned in studies of mediation effects, any form of direct intervention on the mediator is often infeasible. As a result, unmeasured mediator-outcome confounding can seldom be ruled out. We propose semiparametric identification of natural direct and indirect effects in the presence of unmeasured mediator-outcome confounding by leveraging heteroskedasticity restrictions on the observed data law. For inference, we develop semiparametric estimators that remain consistent under partial misspecification of the observed data model. We illustrate the robustness of the proposed estimators through both simulations and an application to evaluate the effect of self-efficacy on fatigue among health care workers during the COVID-19 outbreak.

翻译：虽然在对调解效果的研究中可以随机地确定暴露,但对调解人的任何形式的直接干预往往是不可行的,因此,很难排除未经衡量的调解结果的混淆。我们建议,在未计量的调解结果被利用对所观察到的数据法的异变性限制而混淆的情况下,对自然的直接和间接影响进行半参数识别。为了推断,我们开发了半对称估量器,在所观察到的数据模型的部分偏差下保持一致性。我们通过模拟和用于评估COVID-19爆发期间保健工作者疲劳症的自我有效性对保健工作者疲劳症的影响的应用,说明了拟议的估算器的稳健性。