{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/evaluating-the-impact-of-state-level-public","title":"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_id":"2110.06115","date":"2021-10-12","proceeding":null,"authors":["Angus K. Wong","Laura B. Balzer"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2110.06115v1","url_pdf":"https://arxiv.org/pdf/2110.06115v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"evaluating-the-impact-of-state-level-public","repo_url":"https://github.com/angus-wong/evaluating-the-impact-of-state-level-public-masking-mandates-on-new-covid-19-cases-and-deaths","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}