G-Transformer for Conditional Average Potential Outcome Estimation Over Time
MCML Authors
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
Valentyn Melnychuk
Stefan Feuerriegel
Prof. Dr.
Principal Investigator
Abstract
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.
misc
Preprint
May. 2024Authors
K. Heß • D. Frauen • V. Melnychuk • S. FeuerriegelLinks
Research Area
BibTeXKey: HFM+24