The difference-in-differences (DiD) design is a quasi-experimental method for estimating treatment effects. In staggered DiD with multiple treatment groups and periods, estimation based on the two-way fixed effects model yields negative weights when averaging heterogeneous group-period treatment effects into an overall effect. To address this issue, we first define group-period average treatment effects on the treated (ATT), and then define groupwise, periodwise, dynamic, and overall ATTs nonparametrically, so that the estimands are model-free. We propose doubly robust estimators for these types of ATTs in the form of augmented inverse variance weighting (AIVW). The proposed framework allows time-varying covariates that partially explain the time trends in outcomes. Even if part of the working models is misspecified, the proposed estimators still consistently estimate the parameter of interest. The asymptotic variance can be explicitly computed from influence functions. Under a homoskedastic working model, the AIVW estimator is simplified to an augmented inverse probability weighting (AIPW) estimator. We demonstrate the desirable properties of the proposed estimators through simulation and an application that compares the effects of a parallel admission mechanism with immediate admission on the China National College Entrance Examination.

翻译：双重差分（DiD）设计是一种用于估计处理效应的准实验方法。在包含多处理组和多时期的交错双重差分设计中，基于双向固定效应模型的估计在将异质性组别-时期处理效应平均为整体效应时会产生负权重。为解决该问题，我们首先定义组别-时期层面接受处理者的平均处理效应（ATT），随后以非参数方式定义组别、时期、动态及整体ATT，从而使估计目标具有模型无关性。我们针对这些ATT类型提出了双重稳健估计量，其形式为增强逆方差加权（AIVW）。所提出的框架允许引入能部分解释结果变量时间趋势的时变协变量。即使部分工作模型设定有误，所提估计量仍能一致地估计目标参数。渐近方差可通过影响函数显式计算。在同方差工作模型下，AIVW估计量可简化为增强逆概率加权（AIPW）估计量。我们通过仿真模拟和一项实证应用（比较平行志愿录取机制与即时录取对中国高考的影响）展示了所提估计量的优良性质。