  Counterfactual prediction is a fundamental task in decision-making.
G-computation is a method for estimating expected counterfactual outcomes under
dynamic time-varying treatment strategies. Existing G-computation
implementations have mostly employed classical regression models with limited
capacity to capture complex temporal and nonlinear dependence structures. This
paper introduces G-Net, a novel sequential deep learning framework for
G-computation that can handle complex time series data while imposing minimal
modeling assumptions and provide estimates of individual or population-level
time varying treatment effects. We evaluate alternative G-Net implementations
using realistically complex temporal simulated data obtained from CVSim, a
mechanistic model of the cardiovascular system.
