Path-specific effects constitute a broad class of mediated effects from an exposure to an outcome via one or more causal pathways along a set of intermediate variables. Most of the literature concerning estimation of mediated effects has focused on parametric models, with stringent assumptions regarding unmeasured confounding. We consider semiparametric inference of a path-specific effect when these assumptions are relaxed. In particular, we develop a suite of semiparametric estimators for the effect along a pathway through a mediator, but not through an exposure-induced confounder of that mediator. These estimators have different robustness properties, as each depends on different parts of the likelihood of the observed data. One estimator is locally semiparametric efficient and multiply robust. The latter property implies that machine learning can be used to estimate nuisance functions. We demonstrate these properties, as well as finite-sample properties of all the estimators, in a simulation study. We apply our method to an HIV study, in which we estimate the effect comparing two drug treatments on a patient's average log CD4 count mediated by the patient's level of adherence, but not by previous experience of toxicity, which is clearly affected by which treatment the patient is assigned to and may confound the effect of the patient's level of adherence on their virologic outcome.

中文翻译：

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在存在中介结果混杂的情况下对路径特定效应的半参数估计

路径特定效应构成了一大类介导效应，是通过沿着一组中间变量的一个或多个因果路径暴露于结果的。大多数有关中介效应估计的文献都集中在参数模型上，并对未测量的混杂因素做出了严格的假设。当放宽这些假设时，我们考虑路径特定效应的半参数推断。特别是，我们开发了一套半参数估计器，用于沿着通过中介的路径产生的影响，但不通过该中介的暴露引起的混杂因素。这些估计量具有不同的鲁棒性属性，因为每个估计量都取决于观测数据的可能性的不同部分。一种估计量具有局部半参数效率和乘法鲁棒性。后一个属性意味着机器学习可用于估计干扰函数。我们在模拟研究中展示了这些属性以及所有估计器的有限样本属性。我们将我们的方法应用于 HIV 研究，在该研究中，我们估计了比较两种药物治疗对患者平均 CD4 计数日志的影响，该影响由患者的依从性水平介导，而不是由先前的毒性经历影响，这显然受到哪种治疗的影响患者被分配到并可能会混淆患者的依从性水平对其病毒学结果的影响。