Lockdown effects in US states: an artificial counterfactual approach
This provides evidence on the effectiveness of lockdown policies for public health decision-makers, though it is incremental as it applies an existing method to new data.
The study assessed the impact of lockdowns on COVID-19 cases and deaths in US states using an artificial counterfactual approach, finding that without lockdowns, the accumulated number of cases would have been two times larger on average in the short run.
We adopt an artificial counterfactual approach to assess the impact of lockdowns on the short-run evolution of the number of cases and deaths in some US states. To do so, we explore the different timing in which US states adopted lockdown policies, and divide them among treated and control groups. For each treated state, we construct an artificial counterfactual. On average, and in the very short-run, the counterfactual accumulated number of cases would be two times larger if lockdown policies were not implemented.