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The default nearest neighbor matching method in MATCHIT is ``greedy'' matching, where the closest control match for each treated unit is chosen one at a time, without trying to minimize a global distance measure. In contrast, ``optimal'' matching finds the matched samples with the smallest average absolute distance across all the matched pairs. Gu and Rosenbaum (1993) find that greedy and optimal matching approaches generally choose the same sets of controls for the overall matched samples, but optimal matching does a better job of minimizing the distance within each pair. In addition, optimal matching can be helpful when there are not many appropriate control matches for the treated units.
Optimal matching is performed with MATCHIT by setting method = "optimal", which automatically loads an add-on package called optmatch (Hansen, 2004). The following example can also be run by typing demo(optimal) at the R prompt. We conduct 2:1 optimal ratio matching based on the propensity score from the logistic regression.
> m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "optimal", ratio = 2)