We propose a new semi-parametric model based on Bayesian Additive Regression Trees (BART). In our approach, the response variable is approximated by a linear predictor and a BART model, where the first component is responsible for estimating the main effects and BART accounts for the non-specified interactions and non-linearities. The novelty in our approach lies in the way we change tree generation moves in BART to deal with confounding between the parametric and non-parametric components when they have covariates in common. Through synthetic and real-world examples, we demonstrate that the performance of the new semi-parametric BART is competitive when compared to regression models and other tree-based methods. The implementation of the proposed method is available at https://github.com/ebprado/SP-BART.

翻译：我们提出了以巴伊西亚Additive Recrestition 树(BART)为基础的新的半参数模型。在我们的方法中,反应变量以线性预测器和BART模型相近,前者负责估算主要影响,而BART核算非特定互动和非线性。我们的方法的新颖之处在于我们改变巴伊西亚树的生长方式,以在参数和不参数成分具有共性时处理它们之间的混杂问题。我们通过合成和真实世界的例子,表明新的半参数BART的性能与回归模型和其他基于树木的方法相比具有竞争力。拟议的方法的实施见https://github.com/ebprado/SP-BART。