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Activity Number: 239
Type: Topic Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #318752 View Presentation
Title: Nonparametric Methods for Doubly Robust Estimation of Continuous Treatment Effects
Author(s): Edward Kennedy* and Zongming Ma and Dylan Small
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: causal inference ; semiparametric theory ; machine learning

Continuous treatments (e.g., doses) arise often in practice, but most standard causal effect estimators are limited: they either employ parametric models for the effect curve, or else do not allow for doubly robust covariate adjustment. Double robustness allows one of two nuisance estimators to be misspecified, and is important for protecting against model misspecification as well as reducing sensitivity to the curse of dimensionality. In this work we develop a novel approach for causal dose-response curve estimation that is doubly robust without requiring any parametric assumptions, and which naturally incorporates general off-the-shelf machine learning. We derive asymptotic properties for a kernel-based version of our approach and propose a method for data-driven bandwidth selection. The methods are illustrated in a study of the effect of hospital nurse staffing on excess readmissions penalties.

Authors who are presenting talks have a * after their name.

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