Learning about treatment effects in a new target population under transportability assumptions for relative effect measures
在相对效应可迁移假设下识别新目标人群的处理效应AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接讨论处理效应的跨人群迁移、因果识别及混杂控制。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
在相对效应可迁移假设下识别新目标人群的处理效应
- 研究问题
- 如果试验中的条件风险比或均值比能够迁移到目标人群，如何识别该人群的边际因果效应，这与差值效应迁移有何关系？
- 方法与思路
- 作者以条件相对效应的可迁移性建立识别结果，并对目标人群使用多种处理的情形加入部分可交换性假设，提出可实现的估计量。
- 材料陈述的贡献
- 摘要称，相对效应与差值效应的可迁移性通常不兼容，并给出不同目标处理配置及不同协变量集合下的识别与估计结果。
- 阅读关注点
- 应核对可迁移性和部分可交换性的精确定义、识别所需的数据与支持集，以及迁移协变量和混杂控制协变量如何区分。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收状态未经核验；摘要未完整列出识别条件，也未说明估计量的具体形式及推断性质。
原文摘要
Epidemiologists and applied statisticians often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are ``transportable'' across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures (e.g., conditional risk ratios or mean ratios) are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions, which imply the transportability of both relative and difference effect measures. We then describe how marginal causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., a partial assumption of no unmeasured confounding in the target population). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software.