  As we reach the apex of the COVID-19 pandemic, the most pressing question
facing us is: can we even partially reopen the economy without risking a second
wave? We first need to understand if shutting down the economy helped. And if
it did, is it possible to achieve similar gains in the war against the pandemic
while partially opening up the economy? To do so, it is critical to understand
the effects of the various interventions that can be put into place and their
corresponding health and economic implications. Since many interventions exist,
the key challenge facing policy makers is understanding the potential
trade-offs between them, and choosing the particular set of interventions that
works best for their circumstance. In this memo, we provide an overview of
Synthetic Interventions (a natural generalization of Synthetic Control), a
data-driven and statistically principled method to perform what-if scenario
planning, i.e., for policy makers to understand the trade-offs between
different interventions before having to actually enact them. In essence, the
method leverages information from different interventions that have already
been enacted across the world and fits it to a policy maker's setting of
interest, e.g., to estimate the effect of mobility-restricting interventions on
the U.S., we use daily death data from countries that enforced severe mobility
restrictions to create a "synthetic low mobility U.S." and predict the
counterfactual trajectory of the U.S. if it had indeed applied a similar
intervention. Using Synthetic Interventions, we find that lifting severe
mobility restrictions and only retaining moderate mobility restrictions (at
retail and transit locations), seems to effectively flatten the curve. We hope
this provides guidance on weighing the trade-offs between the safety of the
population, strain on the healthcare system, and impact on the economy.
