📝 SummarXiv

Abstract

Differences-in-differences (DiD) is a causal inference method for observational longitudinal data that assumes parallel expected potential outcome trajectories between treatment groups under the counterfactual scenario where all units receive a specific treatment. In this paper DiD is extended to allow for (i) network dependency, where outcomes, treatments, and covariates may exhibit between-unit correlation, (ii) interference, where treatments can affect outcomes in neighboring units, and (iii) network effect heterogeneity, where effects can vary based on a unit's position in the network. The causal estimand of interest is the network averaged expected exposure effect among units with a specific exposure level, where a unit's exposure is a function of its own treatment and its neighbors' treatments. Under a conditional parallel trends assumption and suitable network dependency conditions, a doubly robust estimator allowing for data-adaptive nuisance function estimation is proposed and shown to be consistent and asymptotically normal. The proposed methods are evaluated in simulations and applied to study the effects of adopting emission control technologies in coal power plants on county-level mortality due to cardiovascular disease.