Abstract This talk considers identification and estimation of the Quantile Treatment Effect on the Treated (QTET) under a straightforward distributional extension of the most commonly invoked Mean Difference in Differences assumption used for identifying the Average Treatment Effect on the Treated (ATT). Identification of the QTET is more complicated than the ATT though because it depends on the unknown dependence between the change in untreated potential outcomes and the initial level of untreated potential outcomes for the treated group. To address this issue, we introduce a new Copula Stability Assumption that says that the missing dependence is constant over time. Under this assumption and when panel data is available, the missing dependence can be recovered, and the QTET is identified. Second, we provide identification results for the case when the identifying assumptions hold conditional on covariates. Under slightly stronger versions of the conditional assumptions, we provide very simple estimators based on propensity score re-weighting. We compare the performance of our method to existing methods for estimating QTETs using Lalonde (1986)’s job training dataset. Using this dataset, we find the performance of our method compares favorably to the performance of existing methods.