Multivariate dynamic mediation analysis under a reinforcement learning framework
强化学习框架下的多变量动态中介分析AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确使用干预演算与结构方程定义和估计中介效应，对应因果推断。
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强化学习框架下的多变量动态中介分析
- 研究问题
- 当多个中介变量条件相关且随时间重复观测时，如何刻画单个中介的效应，同时考虑上游路径与历史滞后影响？
- 方法与思路
- 作者借鉴马尔可夫决策过程，结合时变线性结构方程建立中介过程，以同时干预和干预演算定义效应并开展迭代估计。
- 材料陈述的贡献
- 摘要称推导个别中介效应的闭式表达式，提出迭代估计与自助推断，研究渐近和经验表现，并展示移动健康应用。
- 阅读关注点
- 应核对同时干预下的效应定义、因果识别与马尔可夫条件、路径和历史效应的分解，以及自助推断成立的条件。
- 阅读边界
- 所据为稿件版本未注明的 OpenAlex 索引摘要；摘要未列明识别假设或渐近条件，不能将个别中介效应理解为个体层面的处理效应。
原文摘要
Mediation analysis is an important analytic tool commonly used in a broad range of scientific applications. In this article, we study the problem of mediation analysis when there are multivariate and conditionally dependent mediators, and when the variables are observed over multiple time points. The problem is challenging, because the effect of a mediator involves not only the path from the treatment to this mediator itself at the current time point, but also all possible paths pointed to this mediator from its upstream mediators, as well as the carryover effects from all previous time points. We propose a novel multivariate dynamic mediation analysis approach. Drawing inspiration from the Markov decision process model that is frequently employed in reinforcement learning, we introduce a Markov mediation process paired with a system of time-varying linear structural equation models to formulate the problem. We then formally define the individual mediation effect, built upon the idea of simultaneous interventions and intervention calculus. We next derive the closed-form expression, propose an iterative estimation procedure under the Markov mediation process model and develop a bootstrap method to infer the individual mediation effect. We study both the asymptotic property and the empirical performance of the proposed methodology, and further illustrate its usefulness with a mobile health application.