  At a national-level, we sought to investigate the effect of public masking
mandates on COVID-19 in Fall 2020. Specifically, we aimed to evaluate how the
relative growth of COVID-19 cases and deaths would have differed if all states
had issued a mandate to mask in public by September 1, 2020 versus if all
states had delayed issuing such a mandate. To do so, we applied the Causal
Roadmap, a formal framework for causal and statistical inference. The outcome
was defined as the state-specific relative increase in cumulative cases and in
cumulative deaths {21, 30, 45, 60}-days after September 1. Despite the natural
experiment in state-level masking policies, the causal effect of interest was
not identifiable. Nonetheless, we specified the target statistical parameter as
the adjusted rate ratio (aRR): the expected outcome with early implementation
divided by the expected outcome with delayed implementation, after adjusting
for state-level confounders. To minimize strong estimation assumptions, primary
analyses used targeted maximum likelihood estimation (TMLE) with Super Learner.
After 60-days and at a national-level, early implementation was associated 9%
reduction in new COVID-19 cases (aRR: 0.91; 95%CI: 0.88-0.95) and a 16%
reduction in new COVID-19 deaths (aRR: 0.84; 95%CI: 0.76-0.93). Although lack
of identifiability prohibited causal interpretations, application of the Causal
Roadmap facilitated estimation and inference of statistical associations,
providing timely answers to pressing questions in the COVID-19 response.
