Average direct and indirect causal effects under interference
本文在存在跨单元干扰的潜在结果模型中，定义了二元处理的平均间接效应，并证明其与平均直接效应之和等于一个政策干预的效果，适用于处理干扰问题的实证研究。
Summary We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference under cross-unit interference. Our definition is analogous to the standard definition of the average direct effect and can be expressed without needing to compare outcomes across multiple randomized experiments. We show that the proposed indirect effect satisfies a decomposition theorem stating that in a Bernoulli trial, the sum of the average direct and indirect effects always corresponds to the effect of a policy intervention that infinitesimally increases treatment probabilities. We also consider a number of parametric models for interference and find that our nonparametric indirect effect remains a natural estimand when re-expressed in the context of these models.