发表机构
Rutgers University（罗格斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对有序结果随机实验中传统因果估计量难解释的问题，提出贝叶斯潜变量框架，通过有序概率模型建模潜在结果联合分布，克服现有方法局限，能进行因果推断及敏感性分析，模拟和应用验证了方法对治疗效果评估的有效性。
AI 中文摘要
在许多科学应用中，具有有序结果的随机实验很常见，但诸如平均治疗效果等传统因果估计量难以解释，因为有序类别缺乏有意义的数值间距。我们开发了一个贝叶斯潜变量框架，用于对两个可解释的因果估计量进行连贯的超总体和有限总体推断，这两个估计量量化了治疗有益和严格有益的概率。通过有序概率模型对潜在结果的联合分布进行建模，该方法克服了现有方法的可识别性限制，比非参数界产生更精确的推断。我们还研究了潜在结果之间未知关联的影响，并提出了敏感性分析来评估其影响。模拟研究和对人类头皮健康随机实验的应用表明，该方法提供了对治疗效果的精确且实际相关的评估。
英文摘要
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.
CommentsarXiv admin comment: This version has been removed by arXiv administrators as the submitter did not have the rights to agree to the license at the time of submission Submitter comment: v2 presents the application of the method on a different real-life experimental data
