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Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes

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  • Girma, Sourafel
  • Paton, David

Abstract

Machine learning approaches provide an alternative to traditional fixed effects estimators in causal inference. In particular, double-debiased machine learning (DDML) can control for confounders without making subjective judgements about appropriate functional forms. In this paper, we use DDML to examine the impact of differential Covid-19 vaccination rates on care home mortality and other outcomes. Our approach accommodates fixed effects to account for unobserved heterogeneity. In contrast to standard fixed effects estimates, the DDML results provide some evidence that higher vaccination take-up amongst residents, but not staff, reduced Covid mortality in elderly care homes. However, this effect was relatively small, is not robust to alternative measures of mortality and was restricted to the initial vaccination roll-out period.

Suggested Citation

  • Girma, Sourafel & Paton, David, 2024. "Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes," European Economic Review, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:eecrev:v:170:y:2024:i:c:s0014292124002113
    DOI: 10.1016/j.euroecorev.2024.104882
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    More about this item

    Keywords

    Machine learning; Vaccines; Care homes; Covid-19;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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