Visualization of BART and BARP using SHAP
One Hot Encode
Bayesian Additive Regression Trees with Post-Stratification (BARP)
Census-Based Population Proportions for Covariate Bins (2006)
Decision Plot
Numerical summary of Shapley values from an Explain object
Approximate Shapley Values Computed from the BARP Model
Approximate Shapley Values Computed from a BART Model Fitted using `ba...
Approximate Shapley Values Computed from a BART Model Fitted using `ba...
Approximate Shapley Values
Approximate Shapley Values Computed from a BART Model Fitted using `wb...
A Function for Visualizing the Shapley Values
Visualization of Shapley Values from the BARP Model
A Function for Visualizing the Shapley Values of BART Models
A Function for Visualizing the Shapley Values of BART Models
Survey Data on Support for Gay Marriage (2006)
Waterfall Plot
Complex machine learning models are often difficult to interpret. Shapley values serve as a powerful tool to understand and explain why a model makes a particular prediction. This package computes variable contributions using permutation-based Shapley values for Bayesian Additive Regression Trees (BART) and its extension with Post-Stratification (BARP). The permutation-based SHAP method proposed by Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> is grounded in data obtained via MCMC sampling. Similar to the BART model introduced by Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>, this package leverages Bayesian posterior samples generated during model estimation, allowing variable contributions to be computed without requiring additional sampling. The BART model is designed to work with the following R packages: 'BART' <doi:10.18637/jss.v097.i01>, 'bartMachine' <doi:10.18637/jss.v070.i04>, and 'dbarts' <https://CRAN.R-project.org/package=dbarts>. For XGBoost and baseline adjustments, the approach by Lundberg et al. (2020) <doi:10.1038/s42256-019-0138-9> is also considered. The BARP model proposed by Bisbee (2019) <doi:10.1017/S0003055419000480> was implemented with reference to <https://github.com/jbisbee1/BARP> and is designed to work with modified functions based on that implementation. BARP extends post-stratification by computing variable contributions within each stratum defined by stratifying variables. The resulting Shapley values are visualized through both global and local explanation methods.