Before revision: We contribute to the theoretical literature on difference-in-differences estimation for policy evaluation by allowing the common trend assumption to hold conditional on a high-dimensional covariate set. In particular, the covariates can enter the difference-in-differences model in a very flexible form leading to estimation procedures that involve supervised machine learning methods.
After revision: We contribute to the theoretical literature on difference-in-differences estimation for policy evaluation by allowing the common trend assumption that is only valid after conditioning on covariates. We suggest estimators that allow the covariates to enter the model in a very flexible form leading to estimation procedures that involve supervised machine learning methods.