Balancing covariates is crucial for estimating causal effects in an observational study. A new method, the propensity-score (PS) based overlap weighting method (OW), seems to balance covariates quite well. To our knowledge, no studies have yet compared the OW method with the other types of balancing methods such as the PS-based fine stratification method (FS) and the coarsened exact matching method (CEM). We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628) to balance 20 covariates. The plasmode approach was used to simulate outcomes. In the example, the OW method had the smallest standardized mean differences in all covariates and Mahalanobis balance distance (MB) among the methods. In simulations, the OW method achieved the smallest MB (all scenarios < 0.07), bias (< 0.005), variance of bias, square root of mean squared error, and the largest coverage of the true effect size (>99.8%) than FS and CEM. These findings suggest that the OW method can yield perfect covariates balance and therefore enhance the accuracy of estimation. It can be considered as a regular practical method for balancing covariates.