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Nonparametric methods for doubly robust estimation of continuous treatment effects
UPenn Biostatistics Working Paper (2015)
  • Edward H. Kennedy, University of Pennsylvania
  • Zongming Ma, University of Pennsylvania
  • Matthew D. McHugh, University of Pennsylvania
  • Dylan S. Small, University of Pennsylvania

Continuous treatments (e.g., doses) arise often in practice, but available causal effect estimators require either parametric models for the effect curve or else consistent estimation of a single nuisance function. We propose a novel doubly robust kernel smoothing approach, which requires only mild smoothness assumptions on the effect curve and allows for misspecification of either the treatment density or outcome regression. We derive asymptotic properties and also discuss an approach 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.

  • causal inference,
  • cross-validation,
  • dose-response,
  • efficient influence function,
  • kernel smoothing,
  • semiparametric estimation
Publication Date
July, 2015
Citation Information
Edward H. Kennedy, Zongming Ma, Matthew D. McHugh and Dylan S. Small. "Nonparametric methods for doubly robust estimation of continuous treatment effects" UPenn Biostatistics Working Paper (2015)
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