no code implementations • 15 Sep 2022 • Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Determining subgroups that respond especially well (or poorly) to specific interventions (medical or policy) requires new supervised learning methods tailored specifically for causal inference.
no code implementations • 23 Apr 2022 • Meijiang Wang, Jingyu He, P. Richard Hahn
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.
no code implementations • 3 Jun 2021 • Jingyu He, Nicholas Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
1 code implementation • 9 Feb 2020 • Jingyu He, P. Richard Hahn
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.
no code implementations • 29 May 2019 • Jingyu He, Nicholas G. Polson, Jianeng Xu
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
no code implementations • 4 Oct 2018 • Jingyu He, Saar Yalov, P. Richard Hahn
Bayesian additive regression trees (BART) (Chipman et.
no code implementations • 14 Jun 2018 • P. Richard Hahn, Jingyu He, Hedibert Lopes
This paper develops a slice sampler for Bayesian linear regression models with arbitrary priors.
1 code implementation • 25 Apr 2018 • Guanhao Feng, Jingyu He, Nicholas G. Polson
Deep learning searches for nonlinear factors for predicting asset returns.