Bayesian Additive Regression Trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on set of trees work as weak learners very flexible for predicting in the presence non-linearity high-order interactions. In this paper, we introduce an extension BART, called Model (MOTR-BART), conside...

This work is aimed at finding potential Simpson’s paradoxes in Big Data. Simpson’s paradox (SP) arises when choosing the level of data aggregation for causal inference. It describes the phenomenon where the direction of a cause on an effect is reversed when examining the aggregate vs. disaggregates of a sample or population. The practical decision making dilemma that SP raises is which level of...