We estimated the effect of community-level natural hazard exposure during prior
developmental stages on later anxiety and depression symptoms among young adults and …

Regression by composition is a new and flexible toolkit for building and understanding
statistical models. Focusing here on regression models for a binary outcome conditional on …

Active learning selects the most informative samples from the unlabeled dataset to annotate
in the context of a limited annotation budget. While numerous methods have been proposed …

In recent years, theoretical results and simulation evidence have shown Bayesian additive
regression trees to be a highly-effective method for nonparametric regression. Motivated by …

Active learning selects the most informative samples from the unlabelled dataset to annotate
in the context of a limited annotation budget. While numerous methods have been proposed …

Machine Learning (ML) algorithms are powerful data-driven tools for approximating high-
dimensional or non-linear nuisance functions which are useful in practice because the true …

Average causal effects are averages of (heterogeneous) individual treatment effects (ITEs)
taken over the entire target population. The estimation of average causal effects has been …

Causal analysis is fundamental to science and decision-making. It unravels the structure of
the process underlying the data and estimates the effectiveness of interventions. Deriving …

Bayesian additive regression trees (BART) is a Bayesian tree-based model which can
provide high predictive accuracy in both classification and regression problems. Within the …

Assessing differences in the causal effect of a treatment between subgroups plays important
roles in behavioral sciences. Besides quantifying how much subgroups differ in the …