G-Transformer for Conditional Average Potential Outcome Estimation over Time
K. Hess, D. Frauen, V. Melnychuk, S. Feuerriegel. ArXiv preprint, 2024
Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. Yet, existing neural methods for this task suffer from either (a) bias or (b) large variance. In order to address both limitations, we introduce the G-transformer (GT). Our GT is a novel, neural end-to-end model designed for unbiased, low-variance estimation of conditional average potential outcomes (CAPOs) over time. Specifically, our GT is the first neural model to perform regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our GT across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.
Recommended citation:
@article{hess2024g,
title={G-Transformer for Conditional Average Potential Outcome Estimation over Time},
author={Hess, Konstantin and Frauen, Dennis and Melnychuk, Valentyn and Feuerriegel, Stefan},
journal={arXiv preprint arXiv:2405.21012},
year={2024}
}