Non‐parametric methods for doubly robust estimation of continuous treatment effects. (30th September 2016)

• Record Type: Journal Article
• Authors: Kennedy, Edward H.; Ma, Zongming; McHugh, Matthew D.; Small, Dylan S.
• Is Part Of: Journal of the Royal Statistical Society. Volume 79:Number 4(2017)
• Page Start: 1229
• Page End: 1245
• Publication Date: 2016-09-30
• Subjects: Causal inference -- Dose–response -- Efficient influence function -- Kernel smoothing -- Semiparametric estimation; Statistics -- Periodicals; Great Britain -- Statistics -- Periodicals; 519.2
• DOI: 10.1111/rssb.12212
• Languages: English
• ISSNs: 1369-7412

Abstract

Summary: 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.