论文标题
SCPI：合成控制方法的不确定性定量
scpi: Uncertainty Quantification for Synthetic Control Methods
论文作者
论文摘要
合成控制方法提供了一种使用未经处理单元的加权平均值来量化干预效果的方法，以近似处理单位在没有干预措施的情况下会经历的反事实结果。该方法可用于观察研究中的程序评估和因果推断。我们介绍了使用合成控件，在Python，R和Stata中实现的软件包SCPI进行预测和推理。为了估计或预测治疗效果，该软件包提供了一系列（可能受到惩罚的）方法，以利用最新的优化方法。对于不确定性量化，该软件包提供了Cattaneo，Feng和Titiunik（2021）和Cattaneo，Feng，Palomba和Titiunik（2022）引入的预测间隔方法。该论文包括数值插图和与其他合成控制软件的比较。
The synthetic control method offers a way to quantify the effect of an intervention using weighted averages of untreated units to approximate the counterfactual outcome that the treated unit(s) would have experienced in the absence of the intervention. This method is useful for program evaluation and causal inference in observational studies. We introduce the software package scpi for prediction and inference using synthetic controls, implemented in Python, R, and Stata. For point estimation or prediction of treatment effects, the package offers an array of (possibly penalized) approaches leveraging the latest optimization methods. For uncertainty quantification, the package offers the prediction interval methods introduced by Cattaneo, Feng and Titiunik (2021) and Cattaneo, Feng, Palomba and Titiunik (2022). The paper includes numerical illustrations and a comparison with other synthetic control software.
