论文标题
用于合成控制的预测器选择
Predictor Selection for Synthetic Controls
论文作者
论文摘要
合成控制方法通常依赖于处理单元的预处理特征（称为预测变量）。预测因子的选择及其加权如何在合成控制估计器的性能和解释性中起关键作用。本文提出了使用稀疏合成控制程序的使用，该程序会惩罚用于生成反事实以选择最重要预测因子的预测变量的数量。我们在线性因子模型框架中得出了一个新的模型选择一致性结果，并表明惩罚过程具有更快的平方平方误差收敛速率。通过一项模拟研究，我们表明稀疏的合成控制可以达到较低的偏差，并且比未含有的合成控制具有更好的后处理性能。最后，我们采用该方法来重新研究加利福尼亚州命题99通过的研究，并在增强环境中，并提供大量可用预测指标。
Synthetic control methods often rely on matching pre-treatment characteristics (called predictors) of the treated unit. The choice of predictors and how they are weighted plays a key role in the performance and interpretability of synthetic control estimators. This paper proposes the use of a sparse synthetic control procedure that penalizes the number of predictors used in generating the counterfactual to select the most important predictors. We derive, in a linear factor model framework, a new model selection consistency result and show that the penalized procedure has a faster mean squared error convergence rate. Through a simulation study, we then show that the sparse synthetic control achieves lower bias and has better post-treatment performance than the un-penalized synthetic control. Finally, we apply the method to revisit the study of the passage of Proposition 99 in California in an augmented setting with a large number of predictors available.
