A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention
本文扩展了合成控制方法，用于评估弗吉尼亚州罗阿诺克市社区犯罪干预的效果，通过高维微观数据校准权重、设计置换检验和联合统计量，发现干预确实降低了犯罪但效果不如传统方法显著。
The synthetic control method is an increasingly popular tool for analysis of program efficacy. Here, it is applied to a neighborhood-specific crime intervention in Roanoke, VA, and several novel contributions are made to the synthetic control toolkit. We examine high-dimensional data at a granular level (the treated area has several cases, a large number of untreated comparison cases, and multiple outcome measures). Calibration is used to develop weights that exactly match the synthetic control to the treated region across several outcomes and time periods. Further, we illustrate the importance of adjusting the estimated effect of treatment for the design effect implicit within the weights. A permutation procedure is proposed wherein countless placebo areas can be constructed, enabling estimation of p-values under a robust set of assumptions. An omnibus statistic is introduced that is used to jointly test for the presence of an intervention effect across multiple outcomes and post-intervention time periods. Analyses indicate that the Roanoke crime intervention did decrease crime levels, but the estimated effect of the intervention is not as statistically significant as it would have been had less rigorous approaches been used. Supplementary materials for this article are available online.