Direct effect analyses usually require deciding whether a focal variable is a pre-exposure confounder or a post-exposure mediator. In observational studies, that distinction may be unclear because timing is measured coarsely or the variable reflects an evolving process. Considering the average treatment effect (ATE) and the natural direct effect (NDE) as a common notion of the direct effect when the focal variable is a confounder and a mediator, respectively, we show that, in general, no single observed-data estimand recovers both the ATE when the focal variable is a confounder and the NDE when it is a mediator. Consequently, if a practitioner applies an NDE estimator when the variable is actually pre-exposure, the resulting estimate may have no clear causal interpretation. We identify a no-additive-interaction condition under which these quantities coincide, develop sensitivity bounds for departures from that condition, and propose an alternative model-robust estimand. This estimand equals the ATE when the variable is pre-exposure and an interventional direct effect when it is post-exposure. Moreover, within a natural class of outcome-free stochastic direct effects, it is the unique observed-data functional that remains causally interpretable under both structural roles of the focal variable. We derive an efficient influence function and a doubly robust estimator, yielding robustness at two levels: the estimand is model-robust across the two causal scenarios, and the estimator is doubly robust with respect to nuisance estimation. In simulations and in an NHANES application on elevated PFAS burden, kidney function, and uric acid, mediation-based analyses yielded materially different reported estimates.

翻译：直接效应分析通常需要决定一个焦点变量是暴露前混杂因素还是暴露后中介变量。在观察性研究中，由于时间测量粗糙或变量反映的是演变过程，这种区分可能不明确。将平均处理效应（ATE）和自然直接效应（NDE）分别作为焦点变量为混杂因素和中介变量时的直接效应常见概念，我们证明：一般情况下，不存在单一观测数据估计量能同时恢复焦点变量为混杂因素时的ATE和其为中介变量时的NDE。因此，若实践者将NDE估计量应用于实际为暴露前的变量，所得估计可能缺乏清晰的因果解释。我们识别出这些量值相同时的无加性交互条件，推导该条件偏离时的灵敏度界，并提出一个替代的模型鲁棒估计量。该估计量在变量为暴露前时等于ATE，在变量为暴露后时等于干预直接效应。此外，在一类自然的无结局随机直接效应中，它是唯一在焦点变量两种结构角色下均保持因果可解释性的观测数据泛函。我们推导了有效影响函数和双稳健估计量，实现两个层面的稳健性：估计量对两种因果情景的模型鲁棒，且对伴生估计具有双稳健性。在模拟实验和基于NHANES的高PFAS负荷、肾功能与尿酸关系应用中，基于中介的分析产生了有实质性差异的报告估计值。