论文标题
半参数因果中介分析，具有未衡量的中介结果混淆
Semiparametric causal mediation analysis with unmeasured mediator-outcome confounding
论文作者
论文摘要
尽管可以在调解效应的研究中随机分配暴露，但任何形式的直接干预对介体的干预通常都是不可行的。结果，很少会排除未衡量的调解人结果混淆。我们提出了在存在未衡量的介体结果的情况下，通过利用异方差限制对观察到的数据定律的介体限制，对自然直接和间接效应进行了半参数识别。对于推断，我们开发了半参数估计器，这些估计量在观察到的数据模型的部分错误下保持一致。我们通过仿真和应用程序来说明拟议的估计量，以评估自我效能感对COVID-19期间卫生保健工作者疲劳的影响。
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 misspecifications of the observed data model. We illustrate 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.
