Non-overlap Average Treatment Effect Bounds
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AI 根据所提供的论文材料归类：摘要直接研究重叠失败时总体平均处理效应的部分识别、估计和置信集合。
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无重叠情况下的平均处理效应界
- 研究问题
- 当部分个体只能接受一种处理、重叠条件失败时，能否在结果有界的前提下保留总体平均处理效应作为目标，并给出有效的识别界和区间？
- 方法与思路
- 作者推导无重叠的部分识别界，对非光滑界构造平滑近似，提出目标最小损失估计，并以乘子自助法构造统一有效置信集合。
- 材料陈述的贡献
- 摘要称界宽与无重叠人群规模成比例，估计在所述条件下具根号样本量一致性和渐近正态性，区间覆盖不同规模及平滑参数。
- 阅读关注点
- 应核对结果有界及其他因果识别假设、界的端点与紧性、平滑偏差控制，以及统一有效性是否支持数据驱动选择最窄区间。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列非参数条件与平滑规则，部分识别并未恢复总体平均处理效应的点识别。
原文摘要
The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have non-zero probability of either treatment status, is necessary to identify and estimate the ATE. When overlap fails, the standard solution is to change the estimand, and target a trimmed effect in a subpopulation satisfying overlap. When the outcome is bounded, we demonstrate that this compromise is unnecessary. We derive non-overlap bounds: partial identification bounds on the ATE that do not require overlap. The bounds have width proportional to the size of the non-overlap subpopulation, making them informative in common scenarios when overlap violations are limited. Since the bounds are non-smooth functionals, we derive smooth approximations amenable to semiparametric efficiency theory and propose a Targeted Minimum Loss-Based estimator that is $\sqrt{n}$-consistent and asymptotically normal under nonparametric conditions. A multiplier bootstrap procedure yields uniformly valid confidence sets across all non-overlap subpopulation sizes and smoothing parameters, allowing researchers to report the tightest valid interval. Formally, we compare non-overlap confidence intervals to confidence intervals based on point estimation across multiple overlap regimes. We illustrate the method via simulation studies and real-world data applications.