The Resolution of Causal Heterogeneity
因果异质性的分辨率AI 解读 ↓
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因果异质性的分辨率
- 研究问题
- 没有真实潜在类别时，如何用人口层面的目标描述解释指定比例因果异质性所需的最少分组，并报告组数的不确定性？
- 方法与思路
- 作者定义分辨率剖面，结合交叉拟合、影响函数校正和贝叶斯自助后验，对矩过程开展统一推断，再构造分组及效应报告。
- 材料陈述的贡献
- 摘要称，建立一致条件 Bernstein–von Mises 结果，刻画阈值处单值选择的不可能性，并以同时置信带反演获得有效集合报告。
- 阅读关注点
- 应核对因果特征分布与分辨率定义、辅助估计速率和边际条件，以及阈值附近集合报告和噪声下限诊断的构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列具体条件，应用中的二至三组总结不等于人口存在真实潜在类别。
原文摘要
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.