论文标题
合成控制作为在线线性回归
Synthetic Control As Online Linear Regression
论文作者
论文摘要
本文指出了合成控制和在线学习之间的简单联系。具体而言，我们将合成控制视为跟随领导者（FTL）的实例。因此，在线凸优化的标准结果表明，即使在对手，对治疗单元的反事实结果的综合控制预测选择的结果也几乎和控制单元的结果的甲骨文加权平均值一样。对差异数据的合成控制几乎和甲骨文加权差异差异一样，有可能使其成为实践中的吸引人选择。我们认为，该观察结果进一步支持在比较案例研究中使用合成控制估计值。
This paper notes a simple connection between synthetic control and online learning. Specifically, we recognize synthetic control as an instance of Follow-The-Leader (FTL). Standard results in online convex optimization then imply that, even when outcomes are chosen by an adversary, synthetic control predictions of counterfactual outcomes for the treated unit perform almost as well as an oracle weighted average of control units' outcomes. Synthetic control on differenced data performs almost as well as oracle weighted difference-in-differences, potentially making it an attractive choice in practice. We argue that this observation further supports the use of synthetic control estimators in comparative case studies.
