Regularization of Synthetic Controls for Policy Evaluation
提出了一个统一框架来理解和比较多种合成控制方法，基于均方预测误差界开发了一种更全面的正则化方法，并通过模拟和安慰剂分析验证了其预测反事实的效果。
ABSTRACT We propose a unified framework for interpreting and comparing a broad class of synthetic control (SC) methods. Our framework is built on an analysis of a mean‐squared prediction error (MSPE) bound for the counterfactual predicted by a generic SC method, without imposing a specific outcome model. Using this framework, we develop a generalized SC method that provides a more comprehensive regularization of the MSPE bound than several existing SC methods. Through simulation studies and placebo analyses, we demonstrate the effectiveness of the proposed approach in predicting the counterfactual.