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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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    References listed on IDEAS

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    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LP, vol. 24(1), pages 3-45, March.
    3. Virat Agrawal & Neeraj Sood & Christopher M. Whaley, 2023. "The Impact of the Global COVID-19 Vaccination Campaign on All-Cause Mortality," NBER Working Papers 31812, National Bureau of Economic Research, Inc.
    4. John Gibson, 2023. "Jabbing the economy back to life?," Applied Economics Letters, Taylor & Francis Journals, vol. 30(21), pages 2999-3005, December.
    5. Alexander Karaivanov & Dongwoo Kim & Shih En Lu & Hitoshi Shigeoka, 2022. "COVID-19 vaccination mandates and vaccine uptake," Nature Human Behaviour, Nature, vol. 6(12), pages 1615-1624, December.
    6. Markus B Bjoerkheim & Alex Tabarrok, 2022. "Covid in the nursing homes: the US experience," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 38(4), pages 887-911.
    7. M. Keith Chen & Judith A. Chevalier & Elisa F. Long, 2021. "Nursing home staff networks and COVID-19," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(1), pages 2015455118-, January.
    8. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iv'an Fern'andez-Val, 2017. "Fisher-Schultz Lecture: Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," Papers 1712.04802, arXiv.org, revised Oct 2023.
    9. Newton C. A. da Costa & Francisco Antonio Doria & Jaqueline Vianna & Vitor Rodrigues, 2022. "On the Existence of Universal Vaccines," Review of Behavioral Economics, now publishers, vol. 9(4), pages 379–381-3, November.
    10. John Gibson, 2022. "The Rollout of COVID-19 Booster Vaccines is Associated With Rising Excess Mortality in New Zealand," Working Papers in Economics 22/11, University of Waikato.
    11. Rahi Abouk & John S. Earle & Johanna Catherine Maclean & Sungbin Park, 2024. "Promoting Public Health with Blunt Instruments: Evidence from Vaccine Mandates," NBER Working Papers 32286, National Bureau of Economic Research, Inc.
    12. Butler, David & Butler, Robert & Farnell, Alex & Simmons, Robert, 2024. "COVID-19 infections and short-run worker performance: Evidence from European football," European Journal of Operational Research, Elsevier, vol. 315(2), pages 750-763.
    13. Elizabeth B. Pathak & Janelle M. Menard & Rebecca B. Garcia & Jason L. Salemi, 2022. "Joint Effects of Socioeconomic Position, Race/Ethnicity, and Gender on COVID-19 Mortality among Working-Age Adults in the United States," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
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    1. Coco, Giuseppe & Monturano, Gianluca & Resce, Giuliano, 2025. "Predicting Delays in Cohesion Infrastructure Projects," Economics & Statistics Discussion Papers esdp25099, University of Molise, Department of Economics.

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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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