We propose a new estimator for average causal effects of a binary treatment with panel data in settings with general treatment patterns. Our approach augments the popular two-way-fixed-effects specification with unit-specific weights that arise from a model for the assignment mechanism. We show how to construct these weights in various settings, including the staggered adoption setting, where units opt into the treatment sequentially but permanently. The resulting estimator converges to an average (over units and time) treatment effect under the correct specification of the assignment model, even if the fixed effect model is misspecified. We show that our estimator is more robust than the conventional two-way estimator: it remains consistent if either the assignment mechanism or the two-way regression model is correctly specified. In addition, the proposed estimator performs better than the two-way-fixed-effect estimator if the outcome model and assignment mechanism are locally misspecified. This strong double robustness property underlines and quantifies the benefits of modeling the assignment process and motivates using our estimator in practice. We also discuss an extension of our estimator to handle dynamic treatment effects.

翻译：本文提出了一种新的估计量，用于在具有一般处理模式的场景下，基于面板数据估计二元处理的平均因果效应。我们的方法在流行的双向固定效应规范中引入了由分配机制模型生成的单位特定权重。我们展示了如何在各种场景中构造这些权重，包括交错采纳场景（其中单位依次且永久地选择接受处理）。当分配模型正确设定时，即使在固定效应模型存在错误设定的情况下，由此得到的估计量也会收敛到（跨单位与时间）的平均处理效应。我们证明该估计量比传统的双向估计量更具鲁棒性：只要分配机制或双向回归模型中有任意一个是正确设定的，它就能保持一致性。此外，当结果模型和分配机制存在局部错误设定时，所提估计量比双向固定效应估计量表现更优。这一强双重稳健性特性凸显并量化了建模分配过程的优势，并为在实践中使用我们的估计量提供了动机。本文还讨论了该估计量的扩展以处理动态处理效应。