Bayesian-based Propensity Score Subclassification Estimator

Music City Center 

Subclassification estimators are commonly used to estimate causal effects via the propensity score, offering lower variance compared to weighting methods like inverse probability weighting. Traditionally, the number of strata is set at five without data-driven selection, and even when selected from data, the resulting uncertainty is often ignored. In this study, we propose a novel Bayesian subclassification estimator that accounts for uncertainty in the number of strata rather than selecting a single optimal value. To achieve this, we employ a general Bayesian framework that does not require a likelihood function, avoiding strong assumptions about the outcome model while maintaining flexibility in causal inference. Our proposed method achieves comparable performance to non-Bayesian methods while providing more accurate uncertainty estimation. This approach ensures that uncertainties from the design phase are properly incorporated into the analysis phase, which is often overlooked in conventional methods.

design uncertainty

general Bayes

selection of the number of strata

propensity score

reversible jump MCMC 

Section on Statistics in Epidemiology