Before revision:  We use a sparse regression method to fit the dynamic progression of the data (the Rt parameter) with the least number of changes , allowing us to attribute discrete change events to specific intervention events. Since the sparse fit yields a discrete jump between two constant Rt , we follow do-operator prescriptions and simulate the counterfactual case by forcing Rt to stay at the pre-jump value.
After revision:  We use a sparse regression method to fit the dynamic progression of the data (the Rt parameter) 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 and simulate the counterfactual case by forcing Rt to stay at the pre-jump value.