Difference-in-Differences with Interference

Understand

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.