  In many scenarios, such as the evaluation of place-based policies, potential
outcomes are not only dependent upon the unit's own treatment but also its
neighbors' treatment. Despite this, "difference-in-differences" (DID) type
estimators typically ignore such interference among neighbors. I show in this
paper that the canonical DID estimators generally fail to identify interesting
causal effects in the presence of neighborhood interference. To incorporate
interference structure into DID estimation, I propose doubly robust estimators
for the direct average treatment effect on the treated as well as the average
spillover effects under a modified parallel trends assumption. The approach in
this paper relaxes common restrictions in the literature, such as partial
interference and correctly specified spillover functions. Moreover, robust
inference is discussed based on the asymptotic distribution of the proposed
estimators.
