Is the treatment beneficial? Causal Inference with Ordinal Outcomes: A Bayesian Solution
处理是否有益？有序结局因果推断的贝叶斯方法AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究有序结局的因果有益概率，且目标依赖潜在结局联合分布，因此对应因果推断及分布效应方向。
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处理是否有益？有序结局因果推断的贝叶斯方法
- 研究问题
- 当结局只有等级次序而缺乏可解释的数值间距时，如何推断处理有益及严格有益的概率，并处理潜在结局关联的不识别？
- 方法与思路
- 作者以有序 probit 潜变量模型刻画潜在结局联合分布，将其关联作为敏感性参数，进行超总体与有限总体后验推断。
- 材料陈述的贡献
- 摘要称分析了敏感性参数的非单调影响，模拟区间较非参数界限更窄且覆盖率接近名义水平；收窄来自参数结构。
- 阅读关注点
- 应核对两类有益概率的定义、潜在结局关联参数及推荐范围、先验设定，并检验区间表现对参数模型错设的敏感性。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；潜在结局关联仍不识别，区间收窄不能解释为数据提供了更多识别信息。
原文摘要
Randomized experiments with ordinal outcomes are common across scientific applications, but conventional causal estimands such as the average treatment effect are difficult to interpret because ordinal categories lack meaningful numerical spacing. Two interpretable estimands, the probabilities that the treatment is beneficial and strictly beneficial, depend on the joint distribution of the potential outcomes and are therefore not identifiable from observed data. The sharp nonparametric bounds used to circumvent this are often too wide to be informative. We develop a Bayesian latent variable framework that models the join distribution by an ordered probit model and delivers coherent super population and finite population posterior inference on both estimands. The association between the potential outcomes remains non-identified and is treated as a sensitivity parameter. We characterize its impact on the estimands theoretically and empirically, showing a non-monotonic relationship governed by the location of the treatment effect relative to the roots of their partial derivatives and use this to recommend a range for applied use. Simulations show that posterior intervals are narrower than the bounds while retaining near-nominal coverage, a gain that reflects the parametric structure imposed rather than additional information in the data. Application on a rheumatoid arthritis trial shows that the method provides precise and practically relevant assessments of treatment effectiveness.