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.
Details
Title
- Computational Challenges in BART Modeling: Extrapolation, Classification, and Causal Inference
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)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2024
Subjects
Resource Type
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Note
- Partial requirement for: Ph.D., Arizona State University, 2024
- Field of study: Statistics