Efficient and flexible causal mediation with time-varying mediators, treatments, and confounders
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时变中介、处理与混杂下的高效灵活因果中介分析
- 研究问题
- 存在受既往处理影响的中介与结局混杂因素时，如何在纵向数据中识别干预型中介效应，并允许灵活建模和有效推断？
- 方法与思路
- 作者在无未测混杂与正值性假设下给出识别结果，利用有效影响函数构造序贯回归算法，并允许使用机器学习估计回归。
- 材料陈述的贡献
- 摘要称，估计量在相应条件下具有双重稳健性、根号样本量一致性、渐近正态性及效率，并提供软件和药物比较应用。
- 阅读关注点
- 应核对干预型效应的定义、各时间点识别条件、双重稳健结构、回归收敛率要求，以及置信区间构造与实现细节。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出具体收敛率条件，所述性质不能扩展为任意机器学习算法均有效。
原文摘要
Interventional effects have been proposed as a solution to the unidentifiability of natural (in)direct effects under mediator-outcome confounders affected by the exposure. Such confounders are an intrinsic characteristic of studies with time-varying exposures and mediators, yet the generalization of the interventional effect framework to the time-varying case has received little attention in the literature. We present an identification result for interventional effects in a general longitudinal data structure that allows flexibility in the specification of treatment-outcome, treatment-mediator, and mediator-outcome relationships. Identification is achieved under the standard no-unmeasured-confounders and positivity assumptions. We also present a theoretical and computational study of the properties of the identifying functional based on the efficient influence function (EIF). We use the EIF to propose a sequential regression estimation algorithm that yields doubly robust, $\sqrt{n}$-consistent, asymptotically Gaussian, and efficient estimators under slow convergence rates for the regression algorithms used. This allows the use of flexible machine learning for regression while permitting uncertainty quantification through confidence intervals and p-values. A free and open source \texttt{R} package implementing our proposed estimators is made available on GitHub. We apply the proposed estimator to an application from a comparative effectiveness trial of two medications for opioid use disorder. In the application, we estimate the extent to which differences between the two treatments' on subsequent risk of opioid use is mediated by craving symptoms.