Distributional Random Forest for Complex Survey Designs

Music City Center 

Complex survey data are essential in modern experimental design and healthcare research, enabling cost-effective sampling while mitigating selection bias and improving estimators' statistical efficiency. However, current statistical methodologies offer few nonlinear approaches specifically tailored to complex survey designs—particularly those that model the conditional distribution of multivariate continuous outcomes and possibly their functionals. To bridge this gap, we introduce a novel distributional random forest regression algorithm equipped with strong theoretical guarantees. We illustrate the practical utility of this new algorithm with various biomedical examples from the American NHANES survey cohort.