Bayesian Model Averaging for Propensity Score Analysis

• In Event: Propensity Score Methodology

Abstract

This paper considers Bayesian model averaging as a means of addressing uncertainty in the selection of the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the BMA package, but which ignores the uncertainty in the propensity score. We also provide a fully Bayesian model averaging approach to account for uncertainty in both parameters and models. The properties of the proposed approaches as well as the effects of prior information and Occam's window on treatment effect estimation are evaluated via two simulation studies and a case study. Results show that both Bayesian model averaging propensity score approaches recover the treatment effect estimates well and generally provide larger uncertainty estimates.

Authors

• Jianshen Chen, University of Wisconsin - Madison

• David Kaplan, University of Wisconsin - Madison