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IGC-Net for Conditional Average Potential Outcome Estimation Over Time

MCML Authors

Dennis Frauen

→ Group Stefan Feuerriegel
Artificial Intelligence in Management

Valentyn Melnychuk

→ Group Stefan Feuerriegel
Artificial Intelligence in Management

Stefan Feuerriegel

Prof. Dr.

Principal Investigator

Artificial Intelligence in Management

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.

inproceedings HFM+26