Individual causal effects from observational longitudinal studies with time-varying exposures
时变暴露观测性纵向研究中的个体因果效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究个体因果效应识别，并利用同一个体的纵向重复测量和时变暴露信息。
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时变暴露观测性纵向研究中的个体因果效应
- 研究问题
- 在时变暴露的观测性纵向研究中，重复测量能提供多少个体效应信息，何种条件下可识别给定个体事实信息的因果效应分布？
- 方法与思路
- 作者以潜在接受度因子参数化个体效应修饰，定义给定全部事实信息的跨世界因果效应条件分布，并讨论其识别和重复测量性质。
- 材料陈述的贡献
- 摘要称已知因果结构下，重复测量增加可降低跨世界效应的变异，并在个体效应修饰的跨世界相似性假设下获得识别。
- 阅读关注点
- 应核对跨世界相似性的精确定义、潜在接受度因子的角色、暴露策略要求，以及条件效应分布识别与个体效应点值恢复的区别。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要所述识别依赖跨世界假设，未提供完整统计条件，不能认为重复测量本身足够。
原文摘要
Causal effects may vary among individuals and can even be of opposite signs. When significant effect heterogeneity exists, the population average causal effect might be uninformative for an individual. Due to the fundamental problem of causality, individual causal effects (ICEs) cannot be retrieved from cross-sectional data. However, in crossover studies, it is accepted that ICEs can be estimated under the assumptions of no carryover effects and time invariance of potential outcomes. A generic potential-outcome formulation with appropriate statistical assumptions to identify ICEs is lacking for other longitudinal data with time-varying exposures. We present a general framework for causal effect heterogeneity in which individual-specific effect modification is parameterized with a latent variable, the receptiveness factor. If the exposure varies over time, then the repeated measurements contain information on an individual's level of this receptiveness factor. Therefore, we study the conditional distribution of the ICE given all an individual's factual information. This novel conditional random variable is called the cross-world causal effect (CWCE). For known causal structures and time-varying exposures, the variability of the CWCE reduces with an increasing number of repeated measurements. The CWCE becomes identifiable from observational data under the causal assumption of cross-world similarity of individual-effect modification (i.e. there exists an exposure strategy whose effect is affected by all latent causes). We illustrate the theory with examples in which the cause-effect relations can be parameterized as generalized linear mixed assignments.