Optimal Multilevel Matching Using Network Flows: An Application to a Summer Reading Intervention


S.D. Pimentel, L. Page, M. Lenard, and L. Keele. 2018. “Optimal Multilevel Matching Using Network Flows: An Application to a Summer Reading Intervention.” Annals of Applied Statistics, 12, 3, Pp. 1479-1505. Publisher's Version


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 in- tervention might be administered to all the students in a school. 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 covari- ates but can be slow with larger data sets. Although we cannot di- rectly specify minimal levels of balance for individual covariates, our algorithm is fast and scales easily to larger data sets. We apply this algorithm to assess a school-based intervention through which stu- dents in treated schools were exposed to a new reading program dur- ing summer school. In one variant of the algorithm, where we match both schools and students, we change the causal estimand through optimal subset matching 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 find that the summer intervention does not appear to increase reading test scores. In a sensitivity analysis, however, we determine that an unobserved confounder could easily mask a larger treatment effect.
Last updated on 09/28/2018