Bounds on Extrapolated Treatment Effects in Regression Discontinuity Designs
断点回归设计中外推治疗效应的界AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
本页为预印本版本;arXiv 收录、作者填写的期刊信息以及作者既有发表经历,都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对,只展示已核实例,档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要研究断点回归治疗效应的外推、部分识别及推断,直接属于因果推断。
断点回归设计中外推治疗效应的界AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
External validity is a central concern in regression discontinuity (RD) designs. To assess treatment effect heterogeneity and the representativeness of the conventional RD estimand, this paper studies the identifying content of monotonicity and smoothness restrictions for treatment effects away from the cutoff in both canonical and multi-cutoff RD designs. In the canonical design, monotonicity in the running variable alone has limited identifying power and generally yields only one-sided bounds. In multi-cutoff designs, by contrast, our proposed monotonicity restrictions along both the running-variable and cutoff-group dimensions yield a support-free characterization of the identified set. In both settings, smoothness restrictions may further tighten the identified set, underscoring the value of combining shape and smoothness restrictions. These sharp identified sets, or their outer approximations, are amenable to bias-aware inference and moment-inequality-based inference procedures. An empirical application illustrates the practical usefulness of our approach.