Causal inference with recurrent and competing events
重复事件与竞争事件下的因果推断AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要围绕重复与竞争事件中的因果估计目标、直接和总效应及图示识别条件展开，直接支持因果推断方向。
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重复事件与竞争事件下的因果推断
- 研究问题
- 当同一人的结局可以反复发生，而死亡等竞争事件会终止后续事件时，如何定义并识别具有明确因果含义的处理效应？
- 方法与思路
- 作者使用因果图与单世界干预图讨论识别条件，将重复事件估计目标联系到中介效应，并用计数过程分析离散时间到连续时间的极限。
- 材料陈述的贡献
- 摘要称，研究澄清传统统计目标何时可解释为直接效应或总效应，提出新的干预式目标，并建立与连续时间经典对象的联系。
- 阅读关注点
- 应核对每个目标如何处理竞争事件、相应干预是否可解释，以及图示识别条件与时间细分极限所需的数学条件。
- 阅读边界
- 依据 arXiv 预印本摘要，不能确认同行评审或期刊接收；摘要未列出完整识别假设，也未说明具体估计程序和有限样本性质。
原文摘要
Many research questions concern treatment effects on outcomes that can recur several times in the same individual. For example, medical researchers are interested in treatment effects on hospitalizations in heart failure patients and sports injuries in athletes. Competing events, such as death, complicate causal inference in studies of recurrent events because once a competing event occurs, an individual cannot have more recurrent events. Several statistical estimands have been studied in recurrent event settings, with and without competing events. However, the causal interpretations of these estimands, and the conditions that are required to identify these estimands from observed data, have yet to be formalized. Here we use a formal framework for causal inference to formulate several causal estimands in recurrent event settings, with and without competing events. We clarify when commonly used classical statistical estimands can be interpreted as causal quantities from the causal mediation literature, such as (controlled) direct effects and total effects. Furthermore, we show that recent results on interventionist mediation estimands allow us to define new causal estimands with recurrent and competing events that may be of particular clinical relevance in many subject matter settings. We use causal directed acyclic graphs and single world intervention graphs to illustrate how to reason about identification conditions for the various causal estimands based on subject matter knowledge. Furthermore, using results on counting processes, we show that our causal estimands and their identification conditions, which are articulated in discrete time, converge to classical continuous time counterparts in the limit of fine discretizations of time.