  Censoring occurs when an outcome is unobserved beyond some threshold value.
Methods that do not account for censoring produce biased predictions of the
unobserved outcome. This paper introduces Type I Tobit Bayesian Additive
Regression Tree (TOBART-1) models for censored outcomes. Simulation results and
real data applications demonstrate that TOBART-1 produces accurate predictions
of censored outcomes. TOBART-1 provides posterior intervals for the conditional
expectation and other quantities of interest. The error term distribution can
have a large impact on the expectation of the censored outcome. Therefore the
error is flexibly modeled as a Dirichlet process mixture of normal
distributions.
