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.
翻译:本文章研究实验单位是大型综合体(如市场),而且只有一或少数单位可以接触这种治疗的环境的实验设计。在这种环境中,治疗的随机化可能导致处理和控制组在基线上具有非常不同的特点,产生偏差。我们提出了各种合成控制设计(Abadie, Diamond和Hainmueller,2010年,Abadie和Gardeazabal,2003年),作为在与大型综合体进行的非随机化实验中选择处理单位的实验设计,以及作为控制组使用的未经处理单位的试验设计。平均潜在结果估计为经处理单位的加权平均数,治疗的潜在结果 -- -- 和控制单位的加权平均数,以及未经治疗的潜在结果。我们分析了基于合成控制设计的估计师的特性,并提出新的推断技术。我们表明,在使用综合单位的实验环境中,合成控制设计可以大大减少与治疗随机化相比的估计偏差。