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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Veröffentlicht in:Journal of the Royal Statistical Society. Series B, Statistical methodology Jg. 79; H. 4; S. 1229 - 1245
Hauptverfasser: Kennedy, Edward H., Ma, Zongming, McHugh, Matthew D., Small, Dylan S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Wiley 01.09.2017
Oxford University Press
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ISSN:1369-7412, 1467-9868
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Abstract 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.
AbstractList 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.
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.
Author Small, Dylan S.
Kennedy, Edward H.
Ma, Zongming
McHugh, Matthew D.
Author_xml – sequence: 1
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  surname: McHugh
  fullname: McHugh, Matthew D.
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  givenname: Dylan S.
  surname: Small
  fullname: Small, Dylan S.
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Snippet Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric models for...
Summary Continuous treatments (e.g. doses) arise often in practice, but many available causal effect estimators are limited by either requiring parametric...
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wiley
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StartPage 1229
SubjectTerms 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
Title Non-parametric methods for doubly robust estimation of continuous treatment effects
URI https://www.jstor.org/stable/26773159
https://onlinelibrary.wiley.com/doi/abs/10.1111%2Frssb.12212
https://www.proquest.com/docview/1928299057
https://www.proquest.com/docview/2000567077
Volume 79
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