Search Results for author:
Found 8 papers, 2 papers with code
Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference.
Local Gaussian process extrapolation for BART models with applications to causal inference
Despite this success, standard implementations of BART typically provide inaccurate prediction and overly narrow prediction intervals at points outside the range of the training data.
Bayesian Inference for Gamma Models
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
Stochastic tree ensembles for regularized nonlinear regression
This paper develops a novel stochastic tree ensemble method for nonlinear regression, which we refer to as XBART, short for Accelerated Bayesian Additive Regression Trees.
Data Augementation with Polya Inverse Gamma
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
XBART: Accelerated Bayesian Additive Regression Trees
Bayesian additive regression trees (BART) (Chipman et.
Efficient sampling for Gaussian linear regression with arbitrary priors
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors.
Deep Learning for Predicting Asset Returns
Deep learning searches for nonlinear factors for predicting asset returns.
