Without a credible control group, the most widespread methodologies for estimating causal effects cannot be applied. To fill this gap, we propose the Machine Learning Control Method (MLCM), a new approach for causal panel analysis based on counterfactual forecasting with machine learning. The MLCM estimates policy-relevant causal parameters in short- and long-panel settings without relying on untreated units. We formalize identification in the potential outcomes framework and then provide estimation based on supervised machine learning algorithms. To illustrate the advantages of our estimator, we present simulation evidence and an empirical application on the impact of the COVID-19 crisis on educational inequality in Italy. We implement the proposed method in the companion R package MachineControl.

翻译：在缺乏可信对照组的情况下，最广泛使用的因果效应估计方法难以适用。为填补这一空白，我们提出机器学习控制方法（MLCM），这是一种基于机器学习反事实预测的因果面板分析新方法。该方法无需依赖未处理单元，即可在短面板与长面板设定中估计政策相关的因果参数。我们通过潜在结果框架进行识别形式化，并基于监督学习算法提供估计方案。为验证该估计量的优势，我们呈现仿真证据及一项实证应用——新冠疫情对意大利教育不平等的影响。所提方法已通过配套R包MachineControl实现。