Nested Sensitivity Envelopes for Transported Quantile Treatment Effects
可迁移分位数处理效应的嵌套敏感性包络AI 解读 ↓
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可迁移分位数处理效应的嵌套敏感性包络
- 研究问题
- 当来源研究存在未测量处理混杂，且给定协变量后仍无法直接迁移至目标总体时，如何界定目标总体的分位数处理效应？
- 方法与思路
- 作者分别限制处理分配的赔率比和来源至目标的结果分布似然比，嵌套构造反事实分布包络，再开发正交估计与同时推断。
- 材料陈述的贡献
- 摘要称，论文获得闭式锐利分布包络、分位数及处理效应区间包络边界，并给出规则与非规则情形的推断和二维敏感性前沿。
- 阅读关注点
- 应核对两个敏感性限制的含义、整条分布过程锐利性的证明，以及活跃集合并列、质量点和分布带反演时的推断条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未获核实；摘要未列完整正则条件，处理效应区间包络也不能直接等同于所有联合效应界。
原文摘要
We study target-population quantile treatment effects when a source study may have unmeasured treatment confounding and may not transport to a target population after conditioning on observed covariates. The observed data consist of a source sample with treatment, outcome and covariates, and a target sample with covariates only. We impose two marginal sensitivity restrictions: an odds-ratio bound \(\Gam\) for source treatment assignment and a conditional likelihood-ratio bound \(\Lam\) for source-to-target potential-outcome distribution shift. For each treatment arm and threshold \(y\), we derive a closed-form sharp target counterfactual CDF envelope. The envelope nests a source marginal-sensitivity map inside a target outcome-shift map, preserving two normalizations and generally improving on a single product likelihood-ratio relaxation. We prove process-level sharpness, so the envelopes are attainable as entire CDFs and can be inverted to obtain sharp target quantile bounds and sharp interval-hull QTE bounds. We then develop semiparametric theory for these nonsmooth bound processes. On regular index sets, we give the canonical gradient, including the source propensity contribution required in observational studies, and construct cross-fitted Neyman-orthogonal one-step estimators with uniform Gaussian approximation. On full index sets with active-set ties or mass points, we use Hadamard directional differentiability and subsampling-valid inference, with a primitive finite-support route for the required weak convergence. Finally, we invert simultaneous monotone CDF bands to obtain honest confidence sets for quantile and QTE interval-hull processes, and formulate the two-dimensional \((\Gam,\Lam)\) breakdown frontier as level-set inference for interval-hull non-refutation.