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

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...

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Bibliographic Details
Published in:Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 79; no. 4; pp. 1229 - 1245
Main Authors: Kennedy, Edward H., Ma, Zongming, McHugh, Matthew D., Small, Dylan S.
Format: Journal Article
Language:English
Published: Oxford Wiley 01.09.2017
Oxford University Press
Subjects:
Asymptotic methods
Asymptotic properties
Causal inference
Computer simulation
Density
Dosage
Dose–response
Efficient influence function
equations
hospitals
Kernel smoothing
Nonparametric statistics
Penalties
Property
Regression analysis
Semiparametric estimation
Simulation
Smoothing
Smoothness
Staffing
Statistical methods
Statistics
Treatment methods
ISSN:1369-7412, 1467-9868
Online Access:Get full text
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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.
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ISSN:1369-7412
1467-9868
DOI:10.1111/rssb.12212