On Semi-parametric Inference for BART
Authors: Veronika Rockova
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we continue the theoretical investigation of BART initiated recently by (Roˇckov a and van der Pas, 2017). We focus on statistical inference questions. In particular, we study the Bernstein-von Mises (Bv M) phenomenon (i.e. asymptotic normality) for smooth linear functionals of the regression surface within the framework of nonparametric regression with fixed covariates. Our semi-parametric Bv M results show that, beyond rate-optimal estimation, BART can be also used for valid statistical inference. |
| Researcher Affiliation | Academia | 1Booth School of Business, University of Chicago. Correspondence to: Veronika Roˇckov a <Veronika.Rockova@Chicago Booth.edu>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes a 'standard non-parametric regression setup' with 'fixed (rescaled) predictors' but does not mention the use of a specific, publicly available dataset (e.g., by name, citation, or link). |
| Dataset Splits | No | The paper is theoretical and focuses on statistical inference properties. It does not describe an experimental setup involving training, validation, and test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not detail any software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |