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Biostatistics Seminar: Edward Kennedy, PhD Candidate, University of Pennsylvania
February 4, 2016 @ 3:30 pm - 4:30 pm
University of Pennsylvania
“Robust Causal Inference with Continuous Exposures”
Abstract: Continuous treatments (e.g., doses) arise often in practice, but 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 via simulation and in a study of the effect of hospital nurse staffing on excess readmissions penalties. Light refreshments will be served at 3:00 pm.