# Evaluating the Impact of State-Level Public Masking Mandates on New COVID-19 Cases and Deaths in the United States: A Demonstration of the Causal Roadmap

arXiv: https://arxiv.org/abs/2110.06115 · HTML: https://mlsanity.com/paper/2110.06115/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2021.

## Built on by (most cited first, 3 of 3)

- [Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review](https://mlsanity.com/paper/2303.07329/) (2023)
- [Evaluating shifts in mobility and COVID-19 case rates in U.S. counties: A demonstration of modified treatment policies for causal inference with continuous exposures](https://mlsanity.com/paper/2110.12529/) (2021)
- [Non-parametric efficient estimation of marginal structural models with multi-valued time-varying treatments](https://mlsanity.com/paper/2409.18782/) (2024)

## Abstract (arXiv)

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.