We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...

The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the synthetic control method. In high-dimensional...

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...

Recently, there has been a growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a "treated" unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the...

We consider a new method to estimate causal effects when a treated unit suffers a shock or an intervention, such as a policy change, but there is not a readily available control group or counterfactual. We propose a two-step approach where in the first stage an artificial counterfactual is...

Recently, there has been a growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a “treated” unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the...