发表机构
University of Washington; Emory University; MIT（华盛顿大学; 埃默里大学; 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
本研究刻画了双重差分设计中遗漏变量偏误的公式，提出量化混杂强度的多种指标及敏感性统计量，并给出基于机器学习的推断方法，应用于最低工资对青少年就业的影响评估。
AI 中文摘要
我们研究了经典双重差分（DiD）设计中，当未观测混杂因素导致偏离平行趋势假设时所产生的遗漏变量偏误（OVB）问题。我们的结果为处理组平均处理效应（ATT）的OVB公式提供了一种新颖的刻画，这一结果本身具有独立的研究价值。我们展示了ATT偏误主要受处理分配机制中混杂强度的影响，并提供了量化该强度的多种替代方式，例如：（i）处理组中处理几率平均变化，（ii）处理组与对照组之间混杂不平衡，或（iii）未处理组中处理几率所解释的变异。基于这些结果，我们提供了用于常规报告的敏感性统计量，描述了推翻DiD研究结论所需的最小混杂强度，以及基于与观测协变量或事前趋势比较的混杂强度正式界限。最后，我们为ATT界限提供了灵活且高效的统计推断方法，这些方法可以利用现代机器学习算法进行估计。我们通过一个估计最低工资对青少年就业影响的实证例子展示了我们方法的实用性。
英文摘要
We study the omitted variable bias (OVB) problem in canonical difference-in-differences (DiD) designs when unobserved confounding induces departures from the parallel trends assumption. Our results provide a novel characterization of the OVB formula for the average treatment effect on the treated (ATT), which is of independent interest. We show how the ATT bias is mainly governed by the strength of confounding in the treatment assignment mechanism and provide alternative ways of quantifying this strength, such as (i) changes in the average odds of treatment among the treated, (ii) confounding imbalance between treated and control units, or (iii) variation explained in treatment odds among the untreated. Building on these results, we offer sensitivity statistics for routine reporting, describing the minimum strength of confounding required to overturn the conclusions of a DiD study, as well as formal bounds on the strength of confounders based on comparisons to observed covariates or pre-trends. Finally, we provide flexible and efficient statistical inference methods for the bounds on ATT, which can leverage modern machine learning algorithms for estimation. We demonstrate the utility of our approach in an empirical example that estimates the effects of minimum wage on teen employment.
