arXiv 5 Feb 2025 · Econometrics
arXiv:2502.03600 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces Type 2 Tobit Bayesian Additive Regression Trees (TOBART-2). BART can produce accurate individual-specific treatment effect estimates. However, in practice estimates are often biased by sample selection. We extend the Type 2 Tobit sample selection model to account for nonlinearities and model uncertainty by including sums of trees in both the selection and outcome equations. A Dirichlet Process Mixture distribution for the error terms allows for departure from the assumption of bivariate normally distributed errors. Soft trees and a Dirichlet prior on splitting probabilities improve modeling of smooth and sparse data generating processes. We include a simulation study and an application to the RAND Health Insurance Experiment data set.
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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 | Iqbal, Ogundimu \ Rubio (2023) `Bayesian variable selection in sample selection models using spike-and-slab priors', arXiv preprint arXiv:2312.03538 | 0.843 | 10 | 4 | 60% |
| 2 | Chib, Greenberg \ Jeliazkov (2009) `Estimation of semiparametric models in the presence of endogeneity and sample selection', Journal of Computational and Graphica… | 0.843 | 5 | 3 | 60% |
| 3 | van Hasselt (2011) `Bayesian inference in a sample selection model', Journal of Econometrics 165(2), 221–232 | 0.823 | 52 | 9 | 56% |
| 4 | McCulloch, Sparapani, Logan \ Laud (2021) `Causal inference with the instrumental variable approach and bayesian nonparametric machine learning', arXiv preprint arXiv:210… | 0.822 | 9 | 3 | 56% |
| 5 | Brewer \ Carlson (2024) `Addressing sample selection bias for machine learning methods', Journal of Applied Econometrics | 0.811 | 15 | 3 | 53% |
| 6 | Chipman, George \ McCulloch (2010) `Bart: Bayesian additive regression trees', The Annals of Applied Statistics 4(1), 266–298 | 0.794 | 14 | 4 | 50% |
| 7 | George, Laud, Logan, McCulloch \ Sparapani (2019) Fully nonparametric bayesian additive regression trees, in `Topics in Identification, Limited Dependent Variables, Partial Obser… | 0.763 | 6 | 2 | 67% |
| 8 | Conley, Hansen, McCulloch \ Rossi (2008) `A semi-parametric bayesian approach to the instrumental variable problem', Journal of Econometrics 144(1), 276–305 | 0.737 | 4 | 3 | 50% |
| 9 | van Hasselt (2005) Bayesian sampling algorithms for the sample selection and two-part models, in `Computing in Economics and Finance', Vol | 0.737 | 4 | 3 | 50% |
| 10 | Ding (2014) `Bayesian robust inference of sample selection using selection-t models', Journal of Multivariate Analysis 124, 451–464 | 0.725 | 21 | 7 | 38% |
Showing the top 10 of 78 scored citations.