Meijiang Wang, Jingyu He, P. Richard Hahn
arXiv 23 Apr 2022 · Statistics — Methodology · publishedJournal of Computational and Graphical Statistics (2023) · 8 citations (OpenAlex)
arXiv:2204.10963 · PDF · DOI · OpenAlex · Extracted main text
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+.
appendix boundary found by appendix_command · 96% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | He, J. and P. R. Hahn (2021) Stochastic tree ensembles for regularized nonlinear regression self | 1.000 | 5 | 4 | 100% |
| 2 | He, J., S. Yalov, and P. R. Hahn (2019) XBART: Accelerated Bayesian additive regression trees self | 0.928 | 4 | 3 | 100% |
| 3 | Krantsevich, N., J. He, and P. R. Hahn (2022) Stochastic tree ensembles for estimating heterogeneous effects | 0.928 | 4 | 3 | 100% |
| 4 | Chipman, H. A., E. I. George, R. E. McCulloch, et al (2010) BART: Bayesian additive regression trees | 0.874 | 6 | 2 | 100% |
| 5 | Nethery, R. C., F. Mealli, and F. Dominici (2019) Estimating population average causal effects in the presence of non-overlap: The effect of natural gas compressor station exposu… | 0.874 | 5 | 2 | 100% |
| 6 | Barber, R. F., E. J. Candes, A. Ramdas, and R. J. Tibshirani (2021) Predictive inference with the jackknife+ | 0.737 | 3 | 2 | 100% |
| 7 | Hahn, P. R., J. S. Murray, C. M. Carvalho, et al (2020) Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects self | 0.737 | 3 | 2 | 100% |
| 8 | Zhu, A. Y., N. Mitra, and J. Roy (2023) Addressing positivity violations in causal effect estimation using gaussian process priors | 0.644 | 2 | 2 | 100% |
| 9 | Gramacy, R. B. and H. K. H. Lee (2008) Bayesian treed Gaussian process models with an application to computer modeling | 0.511 | 2 | 1 | 100% |
| 10 | D’Amour, A., P. Ding, A. Feller, L. Lei, and J. Sekhon (2021) Overlap in observational studies with high-dimensional covariates | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 30 scored citations.