  The paper presents an investigation of estimating treatment effect using
different matching methods. The study proposed a new method which is
computationally efficient and convenient in implication-'largest caliper
matching' and compared the performance with other five popular matching methods
by simulation. The bias, empirical standard deviation and the mean square error
of the estimates in the simulation are checked under different treatment
prevalence and different distributions of covariates. A Monte Carlo simulation
study and a real data example are employed to measure the performance of these
methods. It is shown that matched samples improve estimation of the population
treatment effect in a wide range of settings. It reduces the bias if the data
contains the selection on observables and treatment imbalances. Also, findings
about the relative performance of the different matching methods are provided
to help practitioners determine which method should be used under certain
situations.
