发表机构
Rensselaer Polytechnic Institute（伦斯勒理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合多模块的框架，对比多种亚组方法在两个数据集上的假设状态干预策略效用，发现结果依赖假设，无统计显著差异。
AI 中文摘要
传统亚组分析可能得出不稳定且难以解释的结论，尤其是在观察性生物医学数据中，每个个体仅在一种暴露状态下被观察，真实个体处理效应不可用，因果结构不确定。我们研究基于预处理特征（不使用暴露、结局或估计处理效应信息）构建的亚组是否可作为预算受限策略优先级排序的可解释单元。我们提出一个框架，结合因果发现启发的协变量选择、发现-评估样本拆分、归纳无监督聚类、感知不确定性的亚组选择以及留存双稳健策略评估。我们比较K-means、硬模糊C均值、隶属度加权模糊C均值、随机模糊C均值、贝叶斯高斯混合模型（Bayesian GMM），以及由监督因果森林衍生的CATE树作为对照。在PIMA Indians Diabetes数据集上针对假设的肥胖向非肥胖、血糖升高向降低的状态转变，采用70%预算评估策略；在NHANES数据集上针对终身吸烟史对比评估策略。最高估计无门控效用值：采用Bayesian GMM的BMI策略为0.799，采用硬或隶属度加权模糊C均值的血糖策略为0.735，采用K-means的吸烟史策略为0.775。所有成对的95%置信区间均包含0，经Holm校正后无比较仍具统计显著性。贝叶斯池化通常保留无门控分配，而经验伯恩斯坦门控更保守。具有相似估计效用的策略仍可优先选择不同个体。研究结果应被解读为针对假设状态对比的依赖假设的决策支持证据，而非干预获益的证明。
英文摘要
Conventional subgroup analyses can yield unstable and difficult-to-interpret conclusions, especially in observational biomedical data where each individual is observed under only one exposure state, true individual treatment effects are unavailable, and causal structure is uncertain. We investigate whether subgroups constructed from pretreatment characteristics, without using exposure, outcome, or estimated treatment-effect information, can serve as interpretable units for budget-constrained policy prioritization. We propose a framework combining causal-discovery-informed covariate selection, discovery-evaluation sample splitting, inductive unsupervised clustering, uncertainty-aware subgroup selection, and held-out doubly robust policy evaluation. We compare K-means, hard, membership-weighted, and stochastic Fuzzy C-means, Bayesian Gaussian mixture models, and a supervised causal-forest-derived CATE-tree comparator. Policies are evaluated under a 70% budget for hypothetical obesity-to-non-obesity and elevated-to-lower-glucose state shifts in the PIMA Indians Diabetes dataset and for a lifetime-smoking-history contrast in NHANES. The highest estimated ungated utilities were 0.799 for the BMI policy using Bayesian GMM, 0.735 for the glucose policy using hard or membership-weighted FCM, and 0.775 for the smoking-history policy using K-means. All paired 95% confidence intervals for policy-risk differences included zero, and no comparison remained statistically significant after Holm adjustment. Bayesian pooling generally preserved ungated allocations, whereas Empirical Bernstein gating was more conservative. Policies with similar estimated utility could nevertheless prioritize different individuals. The findings should be interpreted as assumption-dependent decision-support evidence for hypothetical state contrasts rather than proof of intervention benefit.
