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arXiv · 2405.21012

IGC-Net for conditional average potential outcome estimation over time

Konstantin Hess·Dennis Frauen·Valentyn Melnychuk·Stefan Feuerriegel
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Abstract

Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent limitations (e.g., division by propensity scores that are often close to zero), which result in poor performance. As a remedy, we introduce the iterative G-computation network (IGC-Net). Our IGC-Net is a novel, neural end-to-end model which adjusts for time-varying confounding in order to estimate conditional average potential outcomes (CAPOs) over time. Specifically, our IGC-Net is the first neural model to perform fully regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our IGC-Net across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.

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Konstantin Hess, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel. 2026-02-17. IGC-Net for conditional average potential outcome estimation over time. https://arxiv.org/abs/2405.21012

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