Date of this Version
Journal of the Royal Statistical Society Series B (Statistical Methodology)
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.
This is the pre-peer reviewed version of the following article: [Kennedy, E.H., Ma, Z., McHugh, M.D., & Small, D.S. (2016). Nonparametric Methods for Doubly Robust Estimation of Continuous Treatment Effects. Journal of the Royal Statistical Society Series B (Statistical Methodology) 79, no. 4: pp. 1229-1245], which has been published in final form at http://dx.doi.org/10.1111/rssb.12212. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
causal inference, dose-response, efficient influence function, kernel smoothing, semiparametric estimation
Kennedy, E. H., Ma, Z., McHugh, M. D., & Small, D. S. (2016). Nonparametric Methods for Doubly Robust Estimation of Continuous Treatment Effects. Journal of the Royal Statistical Society Series B (Statistical Methodology), 79 (4), 1229-1245. http://dx.doi.org/10.1111/rssb.12212
Date Posted: 25 October 2018
This document has been peer reviewed.