Using nonparametric regression trees to estimate different forms of heterogeneous treatment effects
Citation
BibTeX citation:
@inproceedings{buhrman2025,
author = {Buhrman, Graham W. and Liao, Xiangyi. and Kim, Jee-Seon},
editor = {Kim, Jee-Seon and Wu, H. and Sweet, T. M. and Molenaar, D.
and Junker, B. W. and Moustaki, I. and Harring, J. and Bulut, O. and
Tong, X. and Wallin, G. and Di Plinio, S.},
title = {Using Nonparametric Regression Trees to Estimate Different
Forms of Heterogeneous Treatment Effects},
booktitle = {Proceedings of the International Meeting of the
Psychometric Society: The 89th Annual Meeting, Prague, Czech
Republic, 2024},
date = {2025-06-25},
url = {https://doi.org/10.64028/pulr698375},
doi = {10.64028/pulr698375},
langid = {en}
}
For attribution, please cite this work as:
Buhrman, G. W., Liao, Xiangyi., & Kim, J.-S. (2025). Using
nonparametric regression trees to estimate different forms of
heterogeneous treatment effects. In J.-S. Kim, H. Wu, T. M. Sweet, D.
Molenaar, B. W. Junker, I. Moustaki, J. Harring, O. Bulut, X. Tong, G.
Wallin, & S. Di Plinio (Eds.), Proceedings of the International
Meeting of the Psychometric Society: The 89th Annual Meeting, Prague,
Czech Republic, 2024. https://doi.org/10.64028/pulr698375