  Researchers are often interested in evaluating the impact of a policy on the
entire (or specific parts of the) distribution of the outcome of interest. In
this paper, I provide a practical toolkit to recover the whole counterfactual
distribution of the untreated potential outcome for the treated group in
non-experimental settings with staggered treatment adoption by generalizing the
existing quantile treatment effects on the treated (QTT) estimator proposed by
Callaway and Li (2019). Besides the QTT, I consider different approaches that
anonymously summarize the quantiles of the distribution of the outcome of
interest (such as tests for stochastic dominance rankings) without relying on
rank invariance assumptions. The finite-sample properties of the estimator
proposed are analyzed via different Monte Carlo simulations. Despite being
slightly biased for relatively small sample sizes, the proposed method's
performance increases substantially when the sample size increases.
