Get our free extension to see links to code for papers anywhere online!Free add-on: code for papers everywhere!Free add-on: See code for papers anywhere!

Add to Chrome

Add to Firefox

Add to Edge

Meijiang Wang

Local Gaussian process extrapolation for BART models with applications to causal inference

Apr 23, 2022
Meijiang Wang, Jingyu He, P. Richard Hahn

Figure 1 for Local Gaussian process extrapolation for BART models with applications to causal inference

Figure 2 for Local Gaussian process extrapolation for BART models with applications to causal inference

Figure 3 for Local Gaussian process extrapolation for BART models with applications to causal inference

Figure 4 for Local Gaussian process extrapolation for BART models with applications to causal inference

Bayesian additive regression trees (BART) is a semi-parametric regression model offering state-of-the-art performance on out-of-sample prediction. Despite this success, standard implementations of BART typically provide inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This paper proposes a novel extrapolation strategy that grafts Gaussian processes to the leaf nodes in BART for predicting points outside the range of the observed data. The new method is compared to standard BART implementations and recent frequentist resampling-based methods for predictive inference. We apply the new approach to a challenging problem from causal inference, wherein for some regions of predictor space, only treated or untreated units are observed (but not both). In simulations studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+.

Via

Access Paper or Ask Questions