G-Transformer for Conditional Average Potential Outcome Estimation over Time
Keywords: causal inference, potential outcomes, treatment effects, G-computation, time-varying confounding, medicine
TL;DR: We develop a novel neural architecture for low-variance estimation of conditional average potential outcomes over time with proper adjustments through regression-based iterative G-computation.
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.
Primary Area: causal reasoning
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Submission Number: 7215
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