Estimating Nursing Home Quality with Selection
利用机器学习中的变分推断技术，估计考虑选择效应的养老院死亡率贝叶斯模型，发现公共报告卡与质量几乎无关，而高质量养老院在疫情期间新冠病例减少2.5%。
Abstract We use variational inference (VI), a technique from the machine learning literature, to estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We demonstrate how one can use VI to quickly and flexibly estimate a high-dimensional economic model with large datasets. Using our facility quality estimates, we examine the correlates of quality and find that public report cards have near-zero correlation. We then show that in contrast to prior literature, higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases.