  We consider the problem of inference in Difference-in-Differences (DID) when
there are few treated units and errors are spatially correlated. We first show
that, when there is a single treated unit, some existing inference methods
designed for settings with few treated and many control units remain
asymptotically valid when errors are weakly dependent. However, these methods
may be invalid with more than one treated unit. We propose alternatives that
are asymptotically valid in this setting, even when the relevant distance
metric across units is unavailable.
