Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment

no code implementations • 8 Mar 2023 • Nathan McJames, Andrew Parnell, Yong Chen Goh, Ann O'Shea

Bayesian Causal Forests (BCF) is a causal inference machine learning model based on a highly flexible non-parametric regression and classification tool called Bayesian Additive Regression Trees (BART).

Causal Inference regression

A Supervised Learning Approach to Rankability

1 code implementation • 14 Mar 2022 • Nathan McJames, David Malone, Oliver Mason

The rankability of data is a recently proposed problem that considers the ability of a dataset, represented as a graph, to produce a meaningful ranking of the items it contains.

Accounting for shared covariates in semi-parametric Bayesian additive regression trees

2 code implementations • 17 Aug 2021 • Estevão B. Prado, Andrew C. Parnell, Keefe Murphy, Nathan McJames, Ann O'Shea, Rafael A. Moral

We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART).

regression