Difference-in-Differences under Local Dependence on Networks

TL;DR

This paper develops a nonparametric difference-in-differences approach to estimate causal effects under local network interference, introducing new estimators with proven asymptotic properties and demonstrating their effectiveness through simulations and real data.

Contribution

It proposes a novel nonparametric identification strategy and estimators for causal effects under local network interference, extending difference-in-differences methodology.

Findings

Effective estimators with asymptotic properties established

Simulation studies confirm estimator performance

Empirical application demonstrates practical utility

Abstract

Estimating causal effects under interference, where the stable unit treatment value assumption is violated, is critical in fields such as regional and public economics. Much of the existing research on causal inference under interference relies on a pre-specified "exposure mapping". This paper focuses on difference-in-difference and proposes a nonparametric identification strategy for direct and indirect average treatment effects under local interference on an observed network. In particular, we proposed a new concept of an indirect effect measuring the total outward influence of the intervension. Based on parallel trends assumption conditional on the neighborhood treatment vector, we develop inverse probability weighted and doubly robust estimators. We establish their asymptotic properties, including consistency under misspecification of nuisance models under some regularity conditions. Simulation studies and an empirical application demonstrate the effectiveness of the proposed method.