microsynth: Synthetic Control Methods for Disaggregated and Micro-Level Data in R

IF 5.4 2区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Statistical Software Pub Date : 2021-01-14 DOI:10.18637/JSS.V097.I02

Michael W Robbins, Steven Davenport

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引用次数: 21

Abstract

The R package microsynth has been developed for implementation of the synthetic control methodology for comparative case studies involving micro- or meso-level data. The methodology implemented within microsynth is designed to assess the efficacy of a treatment or intervention within a well-defined geographic region that is itself a composite of several smaller regions (where data are available at the more granular level for comparison regions as well). The effect of the intervention on one or more time-varying outcomes is evaluated by determining a synthetic control region that resembles the treatment region across pre-intervention values of the outcome(s) and time-invariant covariates and that is a weighted composite of many untreated comparison regions. The microsynth procedure includes functionality that enables its user to (1) calculate weights for synthetic control, (2) tabulate results for statistical inferences, and (3) create time series plots of outcomes for treatment and synthetic control. In this article, microsynth is described in detail and its application is illustrated using data from a drug market intervention in Seattle, WA.

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microsynth: R中分解和微观级数据的综合控制方法

R包microsynth已开发用于实施涉及微观或中观水平数据的比较案例研究的综合控制方法。在microsynth中实现的方法旨在评估在定义明确的地理区域内的治疗或干预措施的效果，该地理区域本身是几个较小区域的组合(其中的数据可以在更细粒度的级别上用于比较区域)。干预对一个或多个时变结果的影响是通过确定一个合成控制区来评估的，该控制区与干预前结果值和时不变协变量的治疗区相似，该控制区是许多未经处理的比较区域的加权组合。microsynth程序包括使用户能够(1)计算合成控制的权重，(2)将统计推断结果制表，以及(3)创建治疗和合成控制结果的时间序列图的功能。在本文中，详细描述了microsynth，并使用来自华盛顿州西雅图药品市场干预的数据说明了其应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Statistical Software 工程技术-计算机：跨学科应用

CiteScore

10.70

自引率

1.70%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.