Coarsened Propensity Scores and Hybrid Estimators for Missing Data and Causal Inference

• Activity Number: 188
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Jack Zhou* and Zhiwei Zhang and Zhaohai Li and Jun Zhang
• Companies: FDA/CDRH and FDA/CDRH and The George Washington University and Shanghai Jiaotong University School of Medicine
• Keywords: Double robustness ; Inverse probability weighting ; Outcome regression ; propensity score ; stratification

Abstract

In the areas of missing data and causal inference, there is great interest in doubly robust (DR) estimators that involve both an outcome regression (OR) model and a propensity score (PS) model. These DR estimators are consistent and asymptotically normal if either model is correctly specified. Despite their theoretical appeal, the practical utility of DR estimators has been disputed. One of the major concerns is the possibility of erratic estimates resulting from near zero denominators due to extreme values of the estimated PS. In contrast, the usual OR estimator based on the OR model alone is efficient when the OR model is correct and generally more stable than the DR estimators, although it can be biased when the OR model is incorrect. In light of the unique advantages of the OR and DR estimators, we propose a class of hybrid estimators that attempt to strike a reasonable balance between the OR and DR estimators. These hybrid estimators are based on coarsened PS estimates, which are less likely to take extreme values and less sensitive to misspecification of the PS model than the original model-based PS estimates. The proposed estimators are compared to existing estimators in si