Electronic Thesis/Dissertation


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

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, a major concern being 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 simulation studies and illustrated with real data from a large observational study on obstetric labor progression and birth outcomes.

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