Type 2 Tobit Sample Selection Models with Bayesian Additive Regression Trees

• URL: http://arxiv.org/abs/2502.03600v1
• Date: Wed, 05 Feb 2025 20:41:40 GMT
• Title: Type 2 Tobit Sample Selection Models with Bayesian Additive Regression Trees
• Authors: Eoghan O'Neill,
• Abstract summary: We extend the Type 2 Tobit sample selection model to account for nonlinearities and model uncertainty. We include a simulation study and an application to the RAND Health Insurance Experiment data set.
• Score: 0.0
• License:
• Abstract: 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.

Related papers

• Joint Models for Handling Non-Ignorable Missing Data using Bayesian Additive Regression Trees: Application to Leaf Photosynthetic Traits Data [0.0]
  Dealing with missing data poses significant challenges in predictive analysis. In cases where the data are missing not at random, jointly modeling the data and missing data indicators is essential. We propose two methods under a selection model framework for handling data with missingness.
  arXiv  Detail & Related papers  (2024-12-19T15:26:55Z)
• Challenges learning from imbalanced data using tree-based models: Prevalence estimates systematically depend on hyperparameters and can be upwardly biased [0.0]
  Imbalanced binary classification problems arise in many fields of study. It is common to subsample the majority class to create a (more) balanced dataset for model training. This biases the model's predictions because the model learns from a dataset that does not follow the same data generating process as new data.
  arXiv  Detail & Related papers  (2024-12-17T19:38:29Z)
• Twice Class Bias Correction for Imbalanced Semi-Supervised Learning [59.90429949214134]
  We introduce a novel approach called textbfTwice textbfClass textbfBias textbfCorrection (textbfTCBC) We estimate the class bias of the model parameters during the training process. We apply a secondary correction to the model's pseudo-labels for unlabeled samples.
  arXiv  Detail & Related papers  (2023-12-27T15:06:36Z)
• On Uncertainty Estimation by Tree-based Surrogate Models in Sequential Model-based Optimization [13.52611859628841]
  We revisit various ensembles of randomized trees to investigate their behavior in the perspective of prediction uncertainty estimation. We propose a new way of constructing an ensemble of randomized trees, referred to as BwO forest, where bagging with oversampling is employed to construct bootstrapped samples. Experimental results demonstrate the validity and good performance of BwO forest over existing tree-based models in various circumstances.
  arXiv  Detail & Related papers  (2022-02-22T04:50:37Z)
• CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator [60.799183326613395]
  We propose an unbiased estimator for categorical random variables based on multiple mutually negatively correlated (jointly antithetic) samples. CARMS combines REINFORCE with copula based sampling to avoid duplicate samples and reduce its variance, while keeping the estimator unbiased using importance sampling. We evaluate CARMS on several benchmark datasets on a generative modeling task, as well as a structured output prediction task, and find it to outperform competing methods including a strong self-control baseline.
  arXiv  Detail & Related papers  (2021-10-26T20:14:30Z)
• Training on Test Data with Bayesian Adaptation for Covariate Shift [96.3250517412545]
  Deep neural networks often make inaccurate predictions with unreliable uncertainty estimates. We derive a Bayesian model that provides for a well-defined relationship between unlabeled inputs under distributional shift and model parameters. We show that our method improves both accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2021-09-27T01:09:08Z)
• Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flows [74.85071867225533]
  Causal mechanisms can be described by structural causal models. One major drawback of state-of-the-art artificial intelligence is its lack of explainability.
  arXiv  Detail & Related papers  (2021-09-06T14:52:58Z)
• Multivariate Probabilistic Regression with Natural Gradient Boosting [63.58097881421937]
  We propose a Natural Gradient Boosting (NGBoost) approach based on nonparametrically modeling the conditional parameters of the multivariate predictive distribution. Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
  arXiv  Detail & Related papers  (2021-06-07T17:44:49Z)
• Inference in Bayesian Additive Vector Autoregressive Tree Models [0.0]
  We propose combining Vector autoregressive ( VAR) models with Bayesian additive regression tree (BART) models. The resulting BAVART model is capable of capturing arbitrary non-linear relations without much input from the researcher. We apply our model to two datasets: the US term structure of interest rates and the Eurozone economy.
  arXiv  Detail & Related papers  (2020-06-29T19:37:09Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.