Abstract
Subgroup analysis is an integral part of comparative analysis where assessing the treatment effect on a response is of central interest. Its goal is to determine the heterogeneity of the treatment effect across subpopulations. In this paper, we adapt the idea of recursive partitioning and introduce an interaction tree (IT) procedure to conduct subgroup analysis. The IT procedure automatically facilitates a number of objectively defined subgroups, in some of which the treatment effect is found prominent while in others the treatment has a negligible or even negative effect. The standard CART (Breiman et al., 1984) methodology is inherited to construct the tree structure. Also, in order to extract factors that contribute to the heterogeneity of the treatment effect, variable importance measure is made available via random forests of the interaction trees. Both simulated experiments and analysis of census wage data are presented for illustration.
Journal Title
Journal of Machine Learning Research
Volume
10
Publication Date
1-1-2009
Document Type
Article
First Page
141
Last Page
158
WOS Identifier
ISSN
1532-4435
Recommended Citation
Su, Xiaogang; Tsai, Chih-Ling; Wang, Hansheng; Nickerson, David M.; and Li, Bogong, "Subgroup Analysis via Recursive Partitioning" (2009). Faculty Bibliography 2000s. 2192.
https://stars.library.ucf.edu/facultybib2000/2192
Comments
Authors: contact us about adding a copy of your work at STARS@ucf.edu