Causal inference methods for treatment effect estimation usually assume independent experimental units. However, this assumption is often questionable because experimental units may interact. We develop augmented inverse probability weighting (AIPW) for estimation and inference of causal treatment effects on dependent observational data. Our framework covers very general cases of spillover effects induced by units interacting in networks. We use plugin machine learning to estimate infinite-dimensional nuisance components leading to a consistent treatment effect estimator that converges at the parametric rate and asymptotically follows a Gaussian distribution. We apply our AIPW method to the Swiss StudentLife Study data to investigate the effect of hours spent studying on exam performance accounting for the students' social network.

翻译：用于治疗效果估计的因果推断方法通常假定为独立的实验单位。然而,这一假设常常令人怀疑,因为实验单位可能相互影响。我们开发了反向概率加权法(AIPW),用于估算和推断依赖性观测数据产生的因果处理效应。我们的框架涵盖由在网络中互动的单位引起的非常一般的外溢效应案例。我们使用插件机来估计无限维干扰成分,从而得出一个一致的治疗效果估计值,该估计值与参数率一致,并且与高斯分布无关。我们用我们的AIPW方法对瑞士学生生活研究数据进行了应用,以调查学生社会网络考试绩效核算所花费的时间的影响。