AI 中文总结
研究因果异质性问题，提出用分辨率概况取代传统亚组数量估计量，通过交叉拟合贝叶斯 - 自举后验等方法进行推理，证明后验与高斯极限合并，模拟和实验验证了方法，能给出分辨率索引报告总结治疗效果异质性。
AI 中文摘要
因果亚组分析常报告少量总结治疗效果异质性的组，仿佛该数量是明确的估计量。然而，在真正的潜在类别总体之外，‘真实’的亚组数量依赖于模型而非总体泛函。我们用新的总体估计量——分辨率概况取代它，它是因果特征律的泛函，给出解释规定比例因果异质性的最少组数，为每个无潜在结构的总体定义。推理围绕单个结构化矩过程的一个交叉拟合贝叶斯 - 自举后验进行，其分数用影响函数校正。一个统一的条件伯恩斯坦 - 冯·米塞斯定理表明该后验在规定的干扰率和边际条件下与有效高斯极限合并。亚组数量的不确定性不是模型选择而是阈值不规则性，分辨率概况是连续路径的整数值阈值，在每个节点处的定律中不连续。在这些节点处，没有单值选择器在根 - n邻域上局部一致，通过反转同步带得到的集值报告在完全相同的扰动上保持局部一致有效性。模拟支持这些近似，对MineThatData电子邮件实验的分析说明了分辨率索引报告，其中两到三组总结访问响应，而更精细的结构低于噪声底诊断。
英文摘要
Causal subgroup analyses often report a small number of groups summarizing treatment effect heterogeneity, as if that number were a well-defined estimand. Outside genuinely latent class populations, however, a ``true'' subgroup count is model dependent rather than a population functional. We replace it with a new population estimand, the resolution profile, a functional of the causal feature law giving the fewest groups explaining a prescribed fraction of causal heterogeneity, defined for every population without latent structure. Inference is organized around one cross-fitted Bayesian-bootstrap posterior for a single structured moment process, its scores corrected with influence functions, so that paths, profiles, fixed-resolution summaries, and subgroup effects follow by composition. A uniform conditional Bernstein--von Mises theorem over a loss class containing the nonsmooth quantization losses shows this posterior merges with the efficient Gaussian limit under stated nuisance-rate and margin conditions. Subgroup-number uncertainty is not model selection but threshold nonregularity, the profile being an integer-valued threshold of a continuous path, discontinuous in the law at each knot. At these knots no single-valued selector is locally uniformly consistent over root-$n$ neighborhoods, and the set-valued report obtained by inverting a simultaneous band retains locally uniform validity over exactly the same perturbations. Simulations support the approximations, and an analysis of the MineThatData e-mail experiment illustrates the resolution-indexed report, in which two to three groups summarize the visit response while finer structure falls below a noise-floor diagnostic.
