Efficient semiparametric estimation of network treatment effects under partial interference

• 成果类型: Article
• 署名作者: Park, C.; Kang, H.
• 署名单位: University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac009
• 发表日期: 2022
• 页码: 10151031
• 关键词: Causal Inference; regression-models; repeated outcomes; propensity score

摘要

Although many estimators for network treatment effects have been proposed, their optimality properties, in terms of semiparametric efficiency, have yet to be resolved. We present a simple yet flexible asymptotic framework for deriving the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one existing estimator, that proposed by , is locally efficient. We also present other estimators that are efficient and discuss results on adaptive estimation. We illustrate application of the efficient estimators in a study of the direct and spillover effects of conditional cash transfer programmes in Colombia.