StochasticTree/stochtree
Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference
This software helps data scientists and researchers build predictive models and analyze the causal impact of different factors. You input your datasets, and it generates an ensemble of 'stochastic trees' to predict outcomes or estimate treatment effects. It's designed for quantitative analysts, statisticians, or anyone needing robust statistical modeling for supervised learning and causal inference.
Use this if you need to build advanced predictive models or estimate the causal effect of an intervention (like a new marketing campaign or drug) on an outcome.
Not ideal if you need simpler, more interpretable models or if your primary goal is basic exploratory data analysis without complex predictive or causal questions.
Stars
71
Forks
17
Language
C++
License
—
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/StochasticTree/stochtree"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python,...
catboost/catboost
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for...
stanfordmlgroup/ngboost
Natural Gradient Boosting for Probabilistic Prediction
lightgbm-org/LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework...
fabsig/GPBoost
Tree-Boosting, Gaussian Processes, and Mixed-Effects Models