  This article studies experimental design in settings where the experimental
units are large aggregate entities (e.g., markets), and only one or a small
number of units can be exposed to the treatment. In such settings,
randomization of the treatment may result in treated and control groups with
very different characteristics at baseline, inducing biases. We propose a
variety of synthetic control designs (Abadie, Diamond and Hainmueller, 2010,
Abadie and Gardeazabal, 2003) as experimental designs to select treated units
in non-randomized experiments with large aggregate units, as well as the
untreated units to be used as a control group. Average potential outcomes are
estimated as weighted averages of treated units, for potential outcomes with
treatment -- and control units, for potential outcomes without treatment. We
analyze the properties of estimators based on synthetic control designs and
propose new inferential techniques. We show that in experimental settings with
aggregate units, synthetic control designs can substantially reduce estimation
biases in comparison to randomization of the treatment.
