发表机构
Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究加权估计量推断问题，通过建立界限构建估计器和置信区间，应用于事件研究和实验，发现部分结果对广泛权重稳健，部分对基线权重小偏差敏感。
AI 中文摘要
研究人员常对加权估计量进行推断，其为组级效应的加权平均。如事件研究和实验中的特定设置。在异质效应下，不同加权方案产生的估计量有不同解释，导致权重选择的模糊性和分歧。本文建立了加权估计量差异的界限和效应异质性的置信界限，用于构建最小化最坏情况偏差的估计器和在加权估计量类上一致有效的置信区间。并将这些方法应用于相关研究，发现某些结果对广泛权重类别具有稳健性，而另一些结果对与基线权重的小偏差敏感。
英文摘要
Researchers often conduct inference on weighted estimands, defined as weighted averages of group-level effects. Example settings include event studies with cohort-level effects and experiments with site-level effects. Under heterogeneous effects, different weighting schemes yield estimands with distinct empirical and policy interpretations, leading to ambiguity and disagreement over the choice of weights. I establish bounds on differences between weighted estimands and confidence bounds on effect heterogeneity, which I use to construct estimators that minimize worst-case bias and confidence intervals that are uniformly valid over classes of weighted estimands. I apply these methods to an event study in Lakdawala, Nakasone, and Kho (2023), which studies the effects of school-based internet access on test scores. I find that results are robust to broad classes of weights. I then apply the methods to Tennessee's Project STAR experiment and find that results are sensitive to small departures from baseline weights.
