In this post, we test both Bayesian Additive Regression Trees (BART) and Causal forests (grf) on four simulated datasets of increasing complexity. May the best method win!
In this post, I explore how we can improve a parametric regression model by comparing its predictions to those of a Random Forest model. This might informs us in what ways the OLS model fails to capture all non-linearities and interactions between the predictors.