Average direct and indirect causal effects under interference

• 成果类型: Article
• 署名作者: Hu, Yuchen; Li, Shuangning; Wager, Stefan
• 署名单位: Stanford University; Stanford University; Stanford University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac008
• 发表日期: 2022
• 页码: 11651172
• 关键词: inference

摘要

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.