Stochastic Tree Ensembles (XBART and BART) for Supervised
Learning and Causal Inference
Flexible stochastic tree ensemble software.
Robust implementations of Bayesian Additive Regression Trees (BART)
(Chipman, George, McCulloch (2010) )
for supervised learning and Bayesian Causal Forests (BCF)
(Hahn, Murray, Carvalho (2020) )
for causal inference. Enables model serialization and parallel sampling
and provides a low-level interface for custom stochastic forest samplers.
Includes the grow-from-root algorithm for accelerated forest sampling
(He and Hahn (2021) ),
a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) ),
and the cloglog BART model of Alam and Linero (2025) for ordinal outcomes.
stochtree R package
Software for building stochastic tree ensembles (i.e. BART, XBART) for supervised learning and causal inference.
Getting started
stochtree can be installed from CRAN via
install.packages("stochtree")
The development version of stochtree can be installed from github via
remotes::install_github("StochasticTree/stochtree", ref="r-dev")