Comparison of the ability of double‐robust estimators to correct bias in propensity score matching analysis. A Monte Carlo simulation study

OBJECTIVE: As covariates are not always adequately balanced after propensity score matching and double- adjustment can be used to remove residual confounding, we compared the performance of several double-robust estimators in different scenarios. METHODS: We conducted a series of Monte Carlo simulations on virtual observational studies. After estimating the propensity scores by logistic regression, we performed 1:1 optimal, nearest-neighbor, …