Jennifer Hill

TL;DR: Bayesian Additive Regression Trees (BART) as discussed by the authors is a Bayesian nonparametric modeling procedure, which can handle a large number of predictors, yields coherent uncertainty intervals, and fluidly handles continuous treatment variables and missing data for the outcome variable.

TL;DR: In this article, the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective, and a multilevel model is proposed to address the multiple comparisons problem and also yield more efficient estimates.

TL;DR: The mi package in R has features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations, and uses Bayesian models and weakly informative prior distributions to construct more stable estimates of imputation models.

TL;DR: This paper uses data from the National Longitudinal Survey of Youth to explore links between mothers’ returns to work within 12 weeks of giving birth and health and developmental outcomes for their children, suggesting causal relationships between early returns toWork and reductions in breastfeeding and immunisations, as well as increases in externalising behaviour problems.

TL;DR: In this article, the authors used data from the National Longitudinal Survey of Youth to explore links between mothers' returns to work within 12 weeks of giving birth and health and developmental outcomes for their children.