Masks and distancing during COVID-19: a causal framework for imputing value to public-health interventions.

ABSTRACT: 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 [Formula: see text] parameter. Then, we postulate each jump in [Formula: see text] as the effect of an intervention. Following the do-operator prescriptions, we simulate the counterfactual case by forcing [Formula: see text] 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 reco

SUBMITTER: Babino A 

PROVIDER: S-EPMC7970858 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature