  In this paper we introduce new, easily implementable designs for drawing
causal inference from randomized experiments on networks with interference.
Inspired by the idea of matching in observational studies, we introduce the
notion of considering a treatment assignment as a quasi-coloring" on a graph.
Our idea of a perfect quasi-coloring strives to match every treated unit on a
given network with a distinct control unit that has identical number of treated
and control neighbors. For a wide range of interference functions encountered
in applications, we show both by theory and simulations that the classical
Neymanian estimator for the direct effect has desirable properties for our
designs. This further extends to settings where homophily is present in
addition to interference.
