We propose a definition for the average indirect effect of a binary treatment in the potential outcomes model for causal inference. 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 universal decomposition theorem, whereby the sum of the average direct and indirect effects always corresponds to the average effect of a policy intervention. We also consider a number of parametric models for interference considered by applied researchers, and find that our (non-parametrically defined) indirect effect remains a natural estimand when re-expressed in the context of these models.

翻译：我们建议对因果推断潜在结果模型中二元处理的平均间接影响作出定义,我们的定义与平均直接影响的标准定义相似,可以表达,无需对多个随机实验的结果进行比较。我们表明,拟议的间接影响满足了一个普遍的分解理论,即平均直接和间接影响的总和总是与政策干预的平均效果相对应。我们还考虑了应用研究人员所考虑的一些干涉的参数模型,发现我们(非对称定义的)间接影响仍然是自然估计,在这些模型中重新表述时,这种间接影响仍然是自然估计的。