Efficient nonparametric estimation with difference-in-differences in the presence of network dependence and interference

TL;DR

This paper extends difference-in-differences methods to handle network dependence, interference, and heterogeneous effects, providing a doubly robust estimator for causal inference in complex observational data.

Contribution

It introduces a new doubly robust estimator that accounts for network dependence, interference, and heterogeneity, with theoretical guarantees and practical evaluation.

Findings

Estimator is consistent, asymptotically normal, and efficient under specified conditions.

Simulation studies demonstrate the estimator's performance in various network scenarios.

Application to emission control data shows the method's practical utility in environmental health studies.

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) non-identically distributed treatment effects and exposure probabilities; (ii) interference, where treatment of one unit can affect outcomes in neighboring units; and (iii) latent variable dependence, where outcomes, treatments, and covariates may exhibit between-unit correlation. The causal estimand of interest is the network-averaged expected exposure effect if units received 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 and heterogeneity conditions, a doubly robust estimator allowing for data-adaptive nuisance function estimation is proposed and shown to be consistent, asymptotically normal, and efficient. 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.