Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes.
用双去偏机器学习方法分析养老院疫苗接种率差异对新冠死亡率的影响，发现居民接种率提高有较小且不稳健的降低死亡效果，但员工接种率无显著影响。
Machine learning approaches provide an alternative approach 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.