Local Gaussian Process Extrapolation for BART Models with Applications to Causal Inference
针对贝叶斯加性回归树（BART）在训练数据范围外预测不准且区间过窄的问题，提出在叶节点嫁接高斯过程的外推策略，在因果推断中仅观测处理或未处理单元的场景下表现优于Jackknife+等方法。
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 suffer from inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data. This article 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 simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+. Supplementary materials for this article are available online.