5 citations · 8 across the 4 of their papers we have counts for
4 papers
Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement
Nathan McJames, Ann O'Shea, Andrew Parnell
Modelling growth in student achievement is a significant challenge in the field of education. Understanding how interventions or experiences such as part-time work can influence th…
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment
Nathan McJames, Andrew Parnell, Yong Chen Goh +1
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 Reg…
A Supervised Learning Approach to Rankability
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. To…
Accounting for shared covariates in semi-parametric Bayesian additive regression trees
Estevão B. Prado, Andrew C. Parnell, Keefe Murphy +3
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART). In the semi-parametric BART paradigm, the response variable is approximated…