In this paper we consider causal mediation analysis when exposures and mediators vary over time. We give non-parametric identification results, discuss parametric implementation, and also provide a weighting approach to direct and indirect effects based on combining the results of two marginal structural models. We also discuss how our results give rise to a causal interpretation of the effect estimates produced from longitudinal structural equation models. When there are time-varying confounders affected by prior exposure and mediator, natural direct and indirect effects are not identified. However, we define a randomized interventional analogue of natural direct and indirect effects that are identified in this setting. The formula that identifies these effects we refer to as the "mediational g-formula." When there is no mediation, the mediational g-formula reduces to Robins' regular g-formula for longitudinal data. When there are no time-varying confounders affected by prior exposure and mediator values, then the mediational g-formula reduces to a longitudinal version of Pearl's mediation formula. However, the mediational g-formula itself can accommodate both mediation and time-varying confounders and constitutes a general approach to mediation analysis with time-varying exposures and mediators.

中文翻译：

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随时间变化的曝光和调解者进行调解分析。

在本文中，当暴露和中介因素随时间变化时，我们考虑因果中介分析。我们给出非参数的识别结果，讨论参数的实现，并基于两个边际结构模型的结果，为直接和间接影响提供加权方法。我们还将讨论我们的结果如何引起由纵向结构方程模型产生的效应估计的因果解释。当有随时间变化的混杂因素受先前的暴露和媒介影响时，自然的直接和间接影响将无法确定。但是，我们定义了在这种情况下确定的自然直接和间接作用的随机干预类似物。标识这些效果的公式称为“中间g公式”。如果没有调解，对于纵向数据，中间g公式简化为Robins的常规g公式。如果没有随时间变化的混杂因素受先前的暴露量和介体值的影响，则介导的g公式可简化为Pearl中介公式的纵向版本。但是，调解g公式本身可以同时容纳调解和时变混杂因素，并且构成了时变暴露和调解者进行调解分析的通用方法。