013: Optimal Full Matching with Restrictions to Limit Large Variation in Matched Sets: Case Study on COVID-19 Mask Mandates

Since the outbreak of the COVID-19 pandemic, masks have been a central topic of public health research. Yet, much of the current literature only evaluates mask mandates retrospectively, allowing for potential bias from unobserved confounding factors. However, propensity score matching methods have been shown to reduce this bias by balancing observed covariates in the hopes of also balancing unobserved covariates.
The result is a synthetic imitation of an experimental study that produces a quasi-causal effect estimate. In our study, we employ propensity score matching methods at a county-wide level to evaluate the effect of mask mandates in August 2020.

Although propensity score matching techniques are not novel to public health research, their use has not yet been implemented to evaluate mask mandates. Further, the matching algorithms common in the literature (eg. pair matching or fixed k:1 matching) are often criticized for discarding valuable data due to their rigid structure. To overcome these challenges, we employ Hansen and Klopfer's optimal full matching algorithm with restrictions. The result is a more precise treatment effect estimate that overcomes both the non-experimental issues in observational studies and the drawbacks of commonly used matching algorithms.

Causal Inference

Propensity Score Matching

COVID-19

Mask Mandate

Optimal Full Matching 

Implementation and Analysis

Conference on Statistical Practice (CSP) 2023