Before revision:  Moreover, they may be relevant alternatives even for settings in which the number of treated units is usually considered as large enough to rely on clustered robust standard errors .
After revision:  Moreover, they may be relevant alternatives even for settings in which the number of treated units is usually considered as large enough to rely on cluster-robust standard errors. We also present an empirical application that highlights some common misunderstandings in the use of randomization inference in DID applications, and illustrates how our results can be used to provide proper inference .