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 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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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. |
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| 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 givenname: Edward H. surname: Kennedy fullname: Kennedy, Edward H. – sequence: 2 givenname: Zongming surname: Ma fullname: Ma, Zongming – sequence: 3 givenname: Matthew D. surname: McHugh fullname: McHugh, Matthew D. – sequence: 4 givenname: Dylan S. surname: Small fullname: Small, Dylan S. |
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| 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 |
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