  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.
