We demonstrate a comprehensive semiparametric approach to causal mediation analysis, addressing the complexities inherent in settings with longitudinal and continuous treatments, confounders, and mediators. Our methodology utilizes a nonparametric structural equation model and a cross-fitted sequential regression technique based on doubly robust pseudo-outcomes, yielding an efficient, asymptotically normal estimator without relying on restrictive parametric modeling assumptions. We are motivated by a recent scientific controversy regarding the effects of invasive mechanical ventilation (IMV) on the survival of COVID-19 patients, considering acute kidney injury (AKI) as a mediating factor. We highlight the possibility of "inconsistent mediation," in which the direct and indirect effects of the exposure operate in opposite directions. We discuss the significance of mediation analysis for scientific understanding and its potential utility in treatment decisions.

翻译：我们提出了一种全面的半参数因果中介分析方法，以应对纵向连续治疗、混杂因素与中介变量共存场景下的固有复杂性。该方法基于非参数结构方程模型与双稳健伪结果交叉拟合序列回归技术，在不依赖参数建模假设的前提下，构建出渐近正态的高效估计量。本研究受近期关于有创机械通气对新冠患者生存影响这一科学争议的驱动，将急性肾损伤视为中介因素。我们特别揭示了"不一致中介"现象的存在可能——即暴露因素的直接效应与间接效应呈现反向作用。最后，本文探讨了中介分析对科学认知的重要意义及其在治疗决策中的潜在应用价值。