We propose a new method for estimating causal effects in longitudinal/panel data settings that we call generalized difference-in-differences. Our approach unifies two alternative approaches in these settings: ignorability estimators (e.g., synthetic controls) and difference-in-differences (DiD) estimators. We propose a new identifying assumption -- a stable bias assumption -- which generalizes the conditional parallel trends assumption in DiD, leading to the proposed generalized DiD framework. This change gives generalized DiD estimators the flexibility of ignorability estimators while maintaining the robustness to unobserved confounding of DiD. We also show how ignorability and DiD estimators are special cases of generalized DiD. We then propose influence-function based estimators of the observed data functional, allowing the use of double/debiased machine learning for estimation. We also show how generalized DiD easily extends to include clustered treatment assignment and staggered adoption settings, and we discuss how the framework can facilitate estimation of other treatment effects beyond the average treatment effect on the treated. Finally, we provide simulations which show that generalized DiD outperforms ignorability and DiD estimators when their identifying assumptions are not met, while being competitive with these special cases when their identifying assumptions are met.

翻译：我们提出一种在纵向/面板数据中估计因果效应的新方法，称之为广义双重差分法。该方法统一了此类数据中的两种替代方法：可忽略性估计量（如合成控制法）与双重差分估计量。我们提出了一种新的识别假设——稳定偏差假设——该假设推广了双重差分法中的条件平行趋势假设，由此构建了广义双重差分框架。这一转变使广义双重差分估计量兼具可忽略性估计量的灵活性，同时保持双重差分法对未观测混杂因素的稳健性。我们还证明了可忽略性估计量与双重差分估计量均为广义双重差分法的特例。随后，我们提出基于影响函数的观测数据泛函估计量，从而允许使用双重/去偏机器学习进行估计。我们进一步展示了广义双重差分法如何便捷扩展至聚类处理分配与分阶段采纳情境，并讨论了该框架如何促进除处理组平均处理效应之外的其他处理效应的估计。最后，仿真实验表明：当可忽略性估计量与双重差分估计量的识别假设未满足时，广义双重差分法表现更优；而当其识别假设满足时，广义双重差分法仍具有与这些特例方法相媲美的竞争力。