Propensity score analysis methods with balancing constraints: A Monte Carlo study.

ABSTRACT: The inverse probability weighting is an important propensity score weighting method to estimate the average treatment effect. Recent literature shows that it can be easily combined with covariate balancing constraints to reduce the detrimental effects of excessively large weights and improve balance. Other methods are available to derive weights that balance covariate distributions between the treatment groups without the involvement of propensity scores. We conducted comprehensive Monte Carlo experiments to study whether the use of covariate balancing constraints circumvent the need for correct propensity score model specification, and whether the use of a propensity score model further improves the estimation performance among methods that use similar covariate balancing constraints. We

SUBMITTER: Li Y 

PROVIDER: S-EPMC9161720 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature