G-Transformer for Conditional Average Potential Outcome Estimation over Time
<|reference_start|>G-Transformer for Conditional Average Potential Outcome Estimation over Time: 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 either (1) do not perform proper adjustments for time-varying confounders, or (2) suffer from large estimation variance. In order to address both limitations, we introduce the G-transformer (GT). Our GT is a novel, neural end-to-end model which adjusts for time-varying confounders, and provides 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.<|reference_end|>
@article{hess2024g-transformer,
    title={G-Transformer for Conditional Average Potential Outcome Estimation over
  Time},
    author={Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel},
    journal={arXiv preprint arXiv:2405.21012},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.21012},
    primaryClass={cs.LG stat.ME}
}