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Activity Number: 115 - HPSS Student Paper Competition Winners: Statistics Advancing Health Policy
Type: Topic Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Health Policy Statistics Section
Abstract #322435
Title: Estimating Average Treatment Effects with a Response-Informed Calibrated Propensity Score
Author(s): David Cheng* and Abhishek Chakrabortty and Ashwin N. Ananthakrishnan and Tianxi Cai
Companies: Harvard T.H. Chan School of Public Health and University of Pennsylvania and Massachusetts General Hospital and Harvard University
Keywords: causal inference ; double-robustness ; electronic medical records ; kernel smoothing ; regularized estimators ; semiparametric efficiency
Abstract:

Adjusting for the propensity score (PS) is a common approach to estimate treatment effects in observational studies. The performance of inverse probability weighting (IPW) and doubly-robust (DR) estimators deteriorate when underlying parametric models for the PS and response are mis-specified or when adjusting for high-dimensional covariates. We propose a response-informed calibrated PS approach that is more robust to model mis-specification and accommodates a large number of covariates while preserving the double-robustness and semi-parametric efficiency properties under correct model specification. Our approach achieves additional robustness and efficiency gain by estimating the PS using a two-dimensional smoothing over an initial parametric PS and another parametric response score. Both of the scores are estimated via regularized regression to accommodate a large number of covariates. Simulations confirm these favorable properties in finite samples. We illustrate the method by estimating the effect of statins on colorectal cancer risk in an electronic medical record (EMR) study and the effect of smoking on C-reactive protein in the Framingham Offspring Study.


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

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