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Abstract:
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Many observational studies of causal effects occur in settings with clustered treatment assignment. In studies of this type, treatment is applied to entire clusters of units. For example, an educational intervention might be administered to all the students in a school. In this paper, we develop a matching algorithm for multilevel data based on a network flow algorithm. Earlier work on multilevel matching relied on integer programming, which allows for balance targeting on specific covariates, but can be slow with larger data sets. While we cannot target balance on specific covariates, our algorithm is quite fast and scales easily to larger data sets. We also consider complications that arise from the common support assumption. In one variant of the algorithm, where we match both schools and students, we change the causal estimand to better maintain common support. In a second variant, we relax the common support assumption to preserve the causal estimand by only matching on schools. We apply both algorithms to a summer school intervention in North Carolina.
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