We consider a general difference-in-differences model in which the treatment variable of interest may be non-binary and its value may change in each time period. It is generally difficult to estimate treatment parameters defined with the potential outcome given the entire path of treatment adoption, because each treatment path may be experienced by only a small number of observations. We propose an alternative approach using the concept of effective treatment, which summarizes the treatment path into an empirically tractable low-dimensional variable, and develop doubly robust identification, estimation, and inference methods. We also provide a companion R software package.

翻译：我们考虑一种广义的差分-差分模型，其中感兴趣的治疗变量可能为非二元变量，且其值在每个时间段可能发生变化。通常，基于整个治疗采用路径定义的治疗参数估计较为困难，因为每条治疗路径可能仅由少量观测样本经历。我们提出一种替代方法，利用有效治疗的概念将治疗路径总结为经验上可处理的低维变量，并开发了双重稳健的识别、估计和推断方法。此外，我们还提供了配套的R软件包。