发表机构
Dalhousie University; Carleton University; McGill University（达尔豪斯大学; 卡尔顿大学; 麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对双重差分法，区分不同维度的亚组处理效应，提出UN-DID和DID-INT估计量，通过CPS模拟分析其推断特性，强调估计目标与方法需匹配政策场景。
AI 中文摘要
具有共同目标和实施日期的政策，可能在细节或背景上存在差异。我们将处理组的平均处理效应（ATT）与按实施队列、辖区、时期或政策类型定义的亚组ATT区分开来。UN-DID和DID-INT是两种构造辖区-时间效应的双重差分（DiD）估计量，它们在与聚合匹配的平行趋势条件下估计这些ATT。在当前人口调查（CPS）的安慰剂法律模拟中，随机化推断的规模通常合适，不过部分辖区特定检验较为保守；刀切法可能对亚组ATT无定义，当有定义时，若处理辖区或对比辖区数量较少，它会过度拒绝原假设。估计目标和推断方法应与政策问题及实施场景相匹配。
英文摘要
Policies with a common objective and implementation date may differ in details or context. We distinguish the aggregate average treatment effect on the treated (ATT) from sub-aggregate ATTs defined by implementation cohort, jurisdiction, period, or policy type. UN-DID and DID-INT, two DiD estimators that construct jurisdiction-by-time effects, estimate these ATTs under parallel-trends conditions matched to the aggregation. In CPS placebo-law simulations, randomization inference is generally well-sized, though some jurisdiction-specific tests are conservative. The jackknife can be undefined for sub-aggregate ATTs; when defined, it over-rejects with few treated or comparison jurisdictions. Estimands and inference methods should match the policy question and implementation setting.
