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Overview: Propensity score methods were developed to facilitate the creation of comparison groups that are similar. ""Similar"" in this sense refers to the distribution of observed characteristics. This research paper describes how to match samples using both local and global optimal matching algorithms. The paper includes macros to perform the nearest available neighbor, caliper, and radius matching methods with or without replacement and matching treated observations to one or many controls. The similarity between observations is evaluated using both the absolute value and the Mahalanobis distance that includes the propensity score along with other covariates.
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