Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
风险比、优势比与风险差：哪种因果效应尺度更易推广？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：摘要直接研究因果效应尺度、处理效应异质性和向目标人群推广的识别要求。
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…
风险比、优势比与风险差：哪种因果效应尺度更易推广？
- 研究问题
- 风险差、风险比和优势比等效应尺度，如何影响可折叠性、处理效应异质性，以及将研究结果推广至目标人群时所需调整的协变量？
- 方法与思路
- 作者回顾常用因果尺度并统一相关定义，通过判别模型讨论效应与基线的关系，再比较推广条件结果和局部效应两条路径的协变量要求。
- 材料陈述的贡献
- 摘要称只有风险差的条件和平均处理效应可不依赖基线，并指出推广所需协变量随效应尺度及识别路径变化，从而细化尺度选择的含义。
- 阅读关注点
- 核对可折叠性、异质性及与基线分离的精确定义，尤其是连续与二元结果下的表述；检查两种推广路径各自要求哪些人群间稳定性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出模型与推广识别条件，不能据此认定风险差在所有研究目标下均优于其他尺度。
原文摘要
There are many measures to report so-called treatment or causal effects: absolute difference, ratio, odds ratio, number needed to treat, and so on. The choice of a measure, e.g. absolute versus relative, is often debated because it leads to different impressions of the benefit or risk of a treatment. Besides, different causal measures may lead to various treatment effect heterogeneity: some input variables may have an influence on some causal measures and no effect at all on others. In addition some measures -- but not all -- have appealing properties such as collapsibility, matching the intuition of a population summary. In this paper, we first review common causal measures and their pros and cons typically brought forward. Doing so, we clarify the notions of collapsibility and treatment effect heterogeneity, unifying existing definitions. Then, we show that for any causal measures there exists a discriminative model such that the conditional average treatment effect (CATE) captures the treatment effect. However, only the risk difference has its CATE and ATE (average treatment effect) disentangled from the baseline, regardless of the outcome type (continuous or binary). As our primary goal is the generalization of causal measures, we show that different sets of covariates are needed to generalize an effect to a target population depending on (i) the causal measure of interest, and (ii) the identification method chosen, that is generalizing either conditional outcome or local effects.