论文标题
BART模型的局部高斯过程推断，并应用于因果推理
Local Gaussian process extrapolation for BART models with applications to causal inference
论文作者
论文摘要
贝叶斯添加剂回归树（BART）是一种半参数回归模型，可在样本外预测上提供最先进的性能。尽管取得了成功，但BART的标准实现通常会在培训数据范围之外的点上提供不准确的预测和过度狭窄的预测间隔。本文提出了一种新型的外推策略，该策略将高斯过程移植到巴特的叶子节点，以预测观察到的数据范围之外的点。将新方法与标准BART实施和最新的基于频繁的基于重新采样的方法进行了比较。我们将新方法应用于因果推论中的一个具有挑战性的问题，其中仅观察到预测空间的某些区域，仅观察到经过处理或未经处理的单元（但两者都不是两者）。在仿真研究中，与流行替代品相比，新方法具有出色的性能。
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 simulation studies, the new approach boasts superior performance compared to popular alternatives, such as Jackknife+.
