The synthetic control method offers a way to estimate the effect of an aggregate 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 \texttt{scpi} for estimation and inference using synthetic controls, implemented in \texttt{Python}, \texttt{R}, and \texttt{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 discussion contains numerical illustrations and a comparison with other synthetic control software.

翻译：合成控制方法提供了一种方法,用未经处理单位的加权平均值来估计综合干预的效果,以估计未经处理单位在没有干预的情况下可能经历的反事实结果。这种方法对观察研究中的方案评估和因果推断有用。我们采用了软件包 \ texttt{scpi},用于使用合成控制进行估计和推断,在\ texttt{Python},\ textt{R}和\ texttt{Stata}中实施。关于治疗效果的点估计或预测,该软件包提供了一系列(可能受到处罚的)方法,利用最新的优化方法。关于不确定性的量化,该软件包提供了Cattaneo、Feng和Titiunik(2021年)和Cattaneo、Feng、Palomba和Titiunik(2022年)采用的预测间隔方法。讨论包括数字插图和与其他合成控制软件的比较。