  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 suffer from either (a) bias or (b) large variance.
In order to address both limitations, we introduce the G-transformer (GT). Our
GT is a novel, neural end-to-end model designed for unbiased, 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.
