  In the context of medical decision making, counterfactual prediction enables
clinicians to predict treatment outcomes of interest under alternative courses
of therapeutic actions given observed patient history. Prior machine learning
approaches for counterfactual predictions under time-varying treatments focus
on static time-varying treatment regimes where treatments do not depend on
previous covariate history. In this work, we present G-Transformer, a
Transformer-based framework supporting g-computation for counterfactual
prediction under dynamic and time-varying treatment strategies. G-Transfomer
captures complex, long-range dependencies in time-varying covariates using a
Transformer architecture. G-Transformer estimates the conditional distribution
of relevant covariates given covariate and treatment history at each time point
using an encoder architecture, then produces Monte Carlo estimates of
counterfactual outcomes by simulating forward patient trajectories under
treatment strategies of interest. We evaluate G-Transformer extensively using
two simulated longitudinal datasets from mechanistic models, and a real-world
sepsis ICU dataset from MIMIC-IV. G-Transformer outperforms both classical and
state-of-the-art counterfactual prediction models in these settings. To the
best of our knowledge, this is the first Transformer-based architecture for
counterfactual outcome prediction under dynamic and time-varying treatment
strategies.
