Visualizable and interpretable regression models with good prediction power

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Manipulating and Measuring Model Interpretability (2018, neutral): “[51] Hyunjoong Kim, Wei-Yin Loh, Yu-Shan Shih, and Probal Chaudhuri.”
BART: Bayesian Additive Regression Trees (2008, methodology): “These data sets are a subset of 52 sets considered by Kim, Loh, Shih & Chaudhuri (2007).”
Optimal trees for prediction and prescription (2018, methodology): “[72] propose a method where each leaf model is limited to two variables, (chosen by stepwise regression) which also has the benefit of improving the interpretability of the model.”
Fifty Years of Classification and Regression Trees (2014, background): “Kim et al. (2007) and Loh (2008b) showed that using only one or two regressor variables in the node models can be useful for data visualization.”