  During the COVID-19 pandemic, the scientific community developed predictive
models to evaluate potential governmental interventions. However, the analysis
of the effects these interventions had is less advanced. Here, we propose a
data-driven framework to assess these effects retrospectively. We use a
regularized regression to find a parsimonious model that fits the data with the
least changes in the Rt parameter. Then, we postulate each jump in Rt as the
effect of an intervention. Following the do-operator prescriptions, we simulate
the counterfactual case by forcing Rt to stay at the pre-jump value. We then
attribute a value to the intervention from the difference between true
evolution and simulated counterfactual. We show that the recommendation to use
facemasks for all activities would reduce the number of cases by 170000 (95% CI
160000 to 180000) in Connecticut, Massachusetts, and New York State. The
framework presented here might be used in any case where cause and effects are
sparse in time.
