  We address estimation of intervention effects in experimental designs in
which (a) interventions are assigned at the cluster level; (b) clusters are
selected to form pairs, matched on observed characteristics; and (c)
intervention is assigned to one cluster at random within each pair. One goal of
policy interest is to estimate the average outcome if all clusters in all pairs
are assigned control versus if all clusters in all pairs are assigned to
intervention. In such designs, inference that ignores individual level
covariates can be imprecise because cluster-level assignment can leave
substantial imbalance in the covariate distribution between experimental arms
within each pair. However, most existing methods that adjust for covariates
have estimands that are not of policy interest. We propose a methodology that
explicitly balances the observed covariates among clusters in a pair, and
retains the original estimand of interest. We demonstrate our approach through
the evaluation of the Guided Care program.
