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) network dependency, where outcomes, treatments, and covariates may exhibit between-unit correlation; and (iii) interference, where treatments can affect outcomes in neighboring units. 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.

翻译：双重差分法是一种用于观测性纵向数据的因果推断方法，其假设在反事实情境下（即所有单元均接受特定处理时），处理组之间的期望潜在结果轨迹保持平行。本文扩展了双重差分法的适用范围，使其能够处理：（i）非同分布的处理效应与暴露概率；（ii）网络依赖性，即结果、处理及协变量可能呈现单元间相关性；（iii）干扰效应，即处理可能影响相邻单元的结果。本研究关注的因果估计量是网络平均期望暴露效应，该效应定义为当单元接受特定暴露水平时的期望效果，其中单元的暴露水平是其自身处理与相邻单元处理的函数。在条件平行趋势假设及适当的网络依赖性与异质性条件下，本文提出了一种允许数据自适应干扰函数估计的双稳健估计量，并证明其具有一致性、渐近正态性与有效性。通过模拟实验评估了所提方法，并将其应用于研究燃煤电厂采用排放控制技术对县级心血管疾病死亡率的影响。