> ML_ALGORITHM // BAYESIAN-ADDITIVE-REGRESSION-TREES-BART_v1.0
Bayesian Additive Regression Trees (BART)
Bayesian ensemble method that fits a sum of decision trees using MCMC backfitting, widely acclaimed for robust causal inference and non-parametric prediction.
Bayesian Non-Parametric Ensemblesbayesian-probabilistichigh-intrinsicmedium (1k-100k)
Back to All AlgorithmsComputational Complexity
Training Complexity:O(mcmc_draws * trees * n * p)
Inference Complexity:O(mcmc_draws * trees * depth)
Hardware Profile
CPU Friendly:Yes
Requires GPU:No
Memory Footprint:moderate
Interpretability & Data
Interpretability Tier:high-intrinsic
Training Data Needs:medium (1k-100k)
Interpretability Assessment
Generates full posterior distributions for Individual Treatment Effects (ITE) and counterfactuals.
Suitable Tasks & Supported Modalities
Suitable Tasks:
regressioncausal inference iteuncertainty quantification
Supported Modalities:
tabular
Implementing Libraries
Foundational Literature
Common Pitfalls & Warnings
- Over-interpreting single posterior trees; individual trees in the sum are not uniquely identifiable