Theory and practice of propensity score analysis

Abstract

Propensity score analysis has been widely used in observational studies to make a causal inference. This study introduces three assumptions for causal inferences-conditional exchangeability, positivity, and consistency-and five steps for propensity score (PS) analysis-1) construct appropriate PS models, 2) check overlap in PS, 3) apply appropriate weighting (inverse probability of treatment weighting, standardized mortality ratio weighting, matching weights, and overlap weights) or matching methods according to the target of inference, 4) check the balance of covariates, and 5) estimate the effect of exposure appropriately. Finally, the advantages of PS analyses over conventional multivariable regression are discussed.