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Causal Inference in the Time of Covid-19

Matteo Bonvini,Edward H. Kennedy,V. Ventura,L. Wasserman

Published 2021 in arXiv: Methodology

ABSTRACT

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 Covid-19 pandemic. We propose a marginal structural model motivated by a modified version of 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 the effect is real, our work highlights the challenges in drawing causal inferences from pandemic data.

PUBLICATION RECORD

  • Publication year

    2021

  • Venue

    arXiv: Methodology

  • Publication date

    2021-03-07

  • Fields of study

    Mathematics, Economics, Psychology

  • Identifiers
    arXiv 2103.04472
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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