Full metadata
Title
Computational Challenges in BART Modeling: Extrapolation, Classification, and Causal Inference
Description
This dissertation centers on Bayesian Additive Regression Trees (BART) and Accelerated BART (XBART) and presents a series of models that tackle extrapolation, classification, and causal inference challenges. To improve extrapolation in tree-based models, I propose a method called local Gaussian Process (GP) that combines Gaussian process regression with trained BART trees. This allows for extrapolation based on the most relevant data points and covariate variables determined by the trees' structure. The local GP technique is extended to the Bayesian causal forest (BCF) models to address the positivity violation issue in causal inference. Additionally, I introduce the LongBet model to estimate time-varying, heterogeneous treatment effects in panel data. Furthermore, I present a Poisson-based model, with a modified likelihood for XBART for the multi-class classification problem.
Date Created
2024
Contributors
- Wang, Meijia (Author)
- Hahn, Paul (Thesis advisor)
- He, Jingyu (Committee member)
- Lan, Shiwei (Committee member)
- McCulloch, Robert (Committee member)
- Zhou, Shuang (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
102 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.191496
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2024
Field of study: Statistics
System Created
- 2024-02-26 11:51:46
System Modified
- 2024-02-26 11:51:50
- 8 months 3 weeks ago
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