.png){kind=icon}
返回
The Augmented Synthetic Control Method
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2021.1929245.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The synthetic control is a weighted average of control units that balances the treated unit's pretreatment outcomes and other covariates as closely as possible. A critical feature of the original proposal is to use SCM only when the fit on pretreatment outcomes is excellent. We propose Augmented SCM as an extension of SCM to settings where such pretreatment fit is infeasible. Analogous to bias correction for inexact matching, augmented SCM uses an outcome model to estimate the bias due to imperfect pretreatment fit and then de-biases the original SCM estimate. Our main proposal, which uses ridge regression as the outcome model, directly controls pretreatment fit while minimizing extrapolation from the convex hull. This estimator can also be expressed as a solution to a modified synthetic controls problem that allows negative weights on some donor units. We bound the estimation error of this approach under different data-generating processes, including a linear factor model, and show how regularization helps to avoid over-fitting to noise. We demonstrate gains from Augmented SCM with extensive simulation studies and apply this framework to estimate the impact of the 2012 Kansas tax cuts on economic growth. We implement the proposed method in the new augsynth R package.
Keyword:
Bias correction
Causal inference
Panel data
Synthetic control
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Ca2+ entry blockers inhibit prostaglandin F2+-induced cerebrovascular contractile responses In goats
PREAI
分享收藏Study on Board-Level Drop Impact Reliability of Sn–Ag–Cu Solder Joint by Considering Strain Rate Dependent Properties of Solder考虑焊料应变率相关特性的板级跌落冲击可靠性研究：Sn–Ag–Cu焊点
PREAI
分享收藏Multifunctional Carbon Nanofibers with Conductive, Magnetic and Superhydrophobic Properties.png)
ChemPhysChem
ChemPhysChemIF0
PREAI
分享收藏Assessing the Causal Effect of Binary Interventions from Observational Panel Data with Few Treated UnitsSTATISTICAL SCIENCE
OAAI
STATISTICAL SCIENCEIF3.4
OAAI分享收藏分享收藏
查看更多内容