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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Vydáno v:	Journal of the Royal Statistical Society. Series B, Statistical methodology Ročník 79; číslo 4; s. 1229 - 1245
Hlavní autoři:	Kennedy, Edward H., Ma, Zongming, McHugh, Matthew D., Small, Dylan S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Oxford Wiley 01.09.2017 Oxford University Press
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1369-7412 1467-9868
DOI:	10.1111/rssb.12212