网络局部依赖下的差分差分法
Difference-in-Differences under Local Dependence on Networks
AI总结：
本文提出了一种非参数识别策略，用于在观察到的网络上局部干扰下的直接和间接平均处理效应估计，基于平行趋势假设和逆概率加权方法。
摘要：
在干扰存在的情况下，即稳定单元处理价值假设被违反时，估计因果效应在区域和公共经济学等领域至关重要。现有大部分关于干扰下的因果推断研究依赖于预定义的“暴露映射”。本文聚焦于差分差分法，并提出了一种非参数识别策略，用于在观察到的网络上局部干扰下的直接和间接平均处理效应。特别是，我们提出了一种新的间接效应概念，用于衡量干预的总向外影响。基于邻里处理向量的平行趋势假设，我们开发了逆概率加权和双重稳健估计量。我们建立了它们的渐近性质，包括在某些正则条件下，即使 nuisance 模型的规格不正确时的一致性。模拟研究和实证应用展示了所提方法的有效性。
英文摘要：
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.
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