This paper studies how to design experiments when the experimental units are large aggregate entities (e.g., markets), and only one or a small number of units can be treated. We propose a class of non-randomized experimental designs using the synthetic control method, which jointly selects treated units and untreated units to serve as a comparison group. We analyze the properties of estimators under these synthetic control designs and develop new inferential techniques. We show that, in experiments with aggregate units, synthetic control designs can substantially reduce estimation bias relative to randomized experiments.