含“不良控制变量”的双重差分法
Difference-in-differences with "bad controls"
- University of Georgia（佐治亚大学）
- Vanderbilt University（范德堡大学）
- University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文针对不良控制变量情形改进双重差分法，推导相关条件并开发两类估计量，扩展至交错式处理场景并应用于失业对收入影响的研究。
中文摘要 AI 辅助
本文探讨当平行趋势假设在对可能受处理影响的协变量（常被称为“不良控制变量”）进行条件设定后成立时的双重差分识别策略。我们表明，直接剔除不良控制变量这类常见做法往往不妥，并开发了两种替代方法，使不良控制变量即便受处理影响也能作为真正的控制变量发挥作用。首先，我们推导了仅对不良控制变量的处理前值进行条件设定的明确条件，这自然引出了以处理前值作为协变量的Callaway和Sant'Anna（2021）估计量。其次，在协变量无混淆条件下，我们开发了可估计处理组平均处理效应的插补法和双重/去偏机器学习估计量。我们将这些结果扩展到交错式处理采用情形，为识别假设提供了预检验，并将这些方法应用于研究失业对收入的影响。
英文摘要
This paper considers difference-in-differences identification strategies when the parallel trends assumption holds after conditioning on covariates that may themselves be affected by the treatment (often referred to as "bad controls"). We show that common approaches such as simply dropping bad controls are often ill-advised and develop two alternative approaches that allow bad controls to function as genuine controls despite being affected by treatment. First, we derive explicit conditions that rationalize conditioning only on pre-treatment values of the bad control, leading naturally to the Callaway and Sant'Anna (2021) estimator with pre-treatment values as covariates. Second, under a covariate unconfoundedness condition, we develop imputation and double/debiased machine learning estimators that recover the average treatment effect on the treated. We extend these results to staggered treatment adoption, provide pre-tests for the identifying assumptions, and apply the methods to study the effects of job displacement on earnings.
