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 whereby, 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 non-parametric indirect effect remains a natural estimand when re-expressed in the context of these models.
翻译:我们建议对跨单位干扰下因果推断潜在结果模型中的二元处理的平均间接影响作出定义,我们的定义类似于平均直接影响的标准定义,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分解的理论,因此,在伯努利试验中,平均直接和间接影响的总和总是与政策干预的效果相对应,这种政策干预极不明显地增加了治疗概率。 我们还考虑了一些干预的参数模型,发现我们的非参数间接影响在重新表述这些模型时仍然是自然估计的。