AI 中文总结
本文针对未知干扰下的面板数据，提出结合凸捐赠者权重有效性边界与定制限制的方法识别处理效应，经自助法校准检验，应用于亚利桑那州工人法时未明确处理效应符号。
AI 中文摘要
当对比单元也可能对处理产生反应时，面板比较会同时反映处理效应与溢出效应。若干扰模式未知，仅观测到的结果无法将二者区分。本文在不要求暴露映射或对受影响的捐赠者进行先验分类的一般限制下，明确了从面板结果中仍可学习到的内容。该框架通过处理前的拟合度为每个凸捐赠者权重缩放有效性边界，并将这些边界与针对应用定制的预先指定限制相结合。有效性边界约束处理效应相对于溢出效应的大小，而额外的限制则确定其可能的取值。这些限制共同产生了一个精准的识别集。当额外限制具有有限线性表示时，检验所提出的处理效应是否与模型兼容，等价于检验有限线性方程组是否有解，自助法校准会对该条件进行检验。在大样本中，反转这些检验可一致控制错误排除每个兼容值的概率。在对《亚利桑那州工人法》的应用中，所得的95%反转集在所有报告的设定中均包含两种符号的效应，未明确处理效应的符号。
英文摘要
When comparison units may also respond to treatment, panel comparisons reflect both the treatment effect and spillovers. If the interference pattern is unknown, observed outcomes alone do not separate the two. I characterize what can nevertheless be learned from panel outcomes under general restrictions, without requiring an exposure mapping or prior classification of affected donors. The framework scales validity bounds for every convex donor weight by its fit before treatment and combines these bounds with prespecified restrictions tailored to the application. The validity bounds constrain the treatment effect relative to spillovers, while the additional restrictions determine its possible values. Together these restrictions yield a sharp identified set. When the additional restrictions have a finite linear representation, checking whether a proposed treatment effect is compatible with the model reduces exactly to asking whether a finite linear system has a solution. Bootstrap calibration tests this condition. Inverting these tests uniformly controls, in large samples, the probability of falsely excluding each compatible value. In an application to the Legal Arizona Workers Act, the resulting 95 percent inversion sets contain effects of both signs across all reported specifications, leaving the sign of the treatment effect unresolved.
Comments72 pages, 3 figures, 3 tables
