Generalised Linear Model Trees with Global Additive Effects

TL;DR

The paper introduces PALM trees, a new model-based tree method that allows for some parameters to be estimated globally while others vary across subgroups, improving subgroup detection in complex data.

Contribution

It extends existing linear model trees by enabling the specification of parameters as globally fixed or locally estimated, enhancing flexibility and subgroup detection capabilities.

Findings

High power in detecting subgroups with global effects

Reliable recovery of true parameters in simulations

Effective detection of treatment-subgroup differences in real data

Abstract

Model-based trees are used to find subgroups in data which differ with respect to model parameters. In some applications it is natural to keep some parameters fixed globally for all observations while asking if and how other parameters vary across subgroups. Existing implementations of model-based trees can only deal with the scenario where all parameters depend on the subgroups. We propose partially additive linear model trees (PALM trees) as an extension of (generalised) linear model trees (LM and GLM trees, respectively), in which the model parameters are specified a priori to be estimated either globally from all observations or locally from the observations within the subgroups determined by the tree. Simulations show that the method has high power for detecting subgroups in the presence of global effects and reliably recovers the true parameters. Furthermore, treatment-subgroup differences are detected in an empirical application of the method to data from a mathematics exam: the PALM tree is able to detect a small subgroup of students that had a disadvantage in an exam with two versions while adjusting for overall ability effects.