A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention

• 成果类型: Article
• 署名作者: Robbins, Michael W.; Saunders, Jessica; Kilmer, Beau
• 署名单位: RAND Corporation; RAND Corporation
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1213634
• 发表日期: 2017
• 页码: 109-126
• 关键词: police; displacement; estimators; VIOLENCE; MARKETS; reduce

摘要

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.