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.

翻译：在干扰条件下估计因果效应，即当稳定单位处理值假设被违反时，对于区域经济学和公共经济学等领域至关重要。现有关于干扰下因果推断的研究大多依赖于预先指定的"暴露映射"。本文聚焦于双重差分法，并提出了一种在观测网络存在局部干扰时，用于识别直接与间接平均处理效应的非参数识别策略。特别地，我们提出了一个新的间接效应概念，用于衡量干预的总向外影响。基于以邻域处理向量为条件的平行趋势假设，我们开发了逆概率加权估计量和双重稳健估计量。我们建立了它们的渐近性质，包括在一些正则条件下即使干扰模型设定错误时仍具有的一致性。模拟研究和一项实证应用证明了所提方法的有效性。