Non-parametric methods for doubly robust estimation of continuous treatment effects

• 成果类型: Article
• 署名作者: Kennedy, Edward H.; Ma, Zongming; McHugh, Matthew D.; Small, Dylan S.
• 署名单位: University of Pennsylvania
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12212
• 发表日期: 2017
• 页码: 1229-1245
• 关键词: semiparametric models; efficient estimation; Causal Inference; regression; questions; answer

摘要

Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for the effect curve, or by not allowing doubly robust covariate adjustment. We develop a novel kernel smoothing approach that requires only mild smoothness assumptions on the effect curve and still allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and give a procedure for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of nurse staffing on hospital readmissions penalties.