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，用于处理纵向连续治疗、混杂因素和中介变量场景中固有的复杂性。该方法采用非参数结构方程模型和基于双重稳健伪结果的交叉拟合序贯回归技术，在不依赖限制性参数建模假设的前提下，得到高效且渐近正态的估计量。本研究受近期关于有创机械通气（IMV）对COVID-19患者生存影响科学争议的启发，将急性肾损伤（AKI）作为中介因素纳入考量。我们揭示了"不一致中介"现象存在的可能性，即暴露的直接效应与间接效应可能呈现相反的作用方向。本文进一步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。