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Full matching is a particular type of subclassification that forms the subclasses in an optimal way (Rosenbaum, 2002; Hansen, 2004). A fully matched sample is composed of matched sets, where each matched set contains one treated unit and one or more controls (or one control unit and one or more treated units). As with subclassification, the only units not placed into a subclass will be those discarded (if a discard option is specified) because they are outside the range of common support. Full matching is optimal in terms of minimizing a weighted average of the estimated distance measure between each treated subject and each control subject within each subclass.
Full matching can be performed with MATCHIT by setting method = "full". Just as with optimal matching, we use the optmatch package (Hansen, 2004), which automatically loads when needed. The following example with full matching (using the default propensity score based on logistic regression) can also be run by typing demo(full) at the R prompt:
> m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "full")