In this paper we develop statistical methods for causal inference in epidemics. Our focus is in estimating the effect of social mobility on deaths in the first year of the Covid-19 pandemic. We propose a marginal structural model motivated by a basic epidemic model. We estimate the counterfactual time series of deaths under interventions on mobility. We conduct several types of sensitivity analyses. We find that the data support the idea that reduced mobility causes reduced deaths, but the conclusion comes with caveats. There is evidence of sensitivity to model misspecification and unmeasured confounding which implies that the size of the causal effect needs to be interpreted with caution. While there is little doubt the effect is real, our work highlights the challenges in drawing causal inferences from pandemic data.
Edward Kennedy gratefully acknowledges support from NSF Grant DMS-1810979.
Ventura and Wasserman are members of the Delphi Group at CMU delphi.cmu.edu. This project arose from their work with Delphi. We are grateful for their help and support. All the data can be obtained from the Delphi website covidcast.cmu.edu. The authors would like to thank Rob Tibshirani and the reviewers for providing helpful feedback on an earlier draft of the paper.
"Causal inference for the effect of mobility on COVID-19 deaths." Ann. Appl. Stat. 16 (4) 2458 - 2480, December 2022. https://doi.org/10.1214/22-AOAS1599