  Estimating the counterfactual outcome of treatment is essential for
decision-making in public health and clinical science, among others. Often,
treatments are administered in a sequential, time-varying manner, leading to an
exponentially increased number of possible counterfactual outcomes.
Furthermore, in modern applications, the outcomes are high-dimensional and
conventional average treatment effect estimation fails to capture disparities
in individuals. To tackle these challenges, we propose a novel conditional
generative framework capable of producing counterfactual samples under
time-varying treatment, without the need for explicit density estimation. Our
method carefully addresses the distribution mismatch between the observed and
counterfactual distributions via a loss function based on inverse probability
re-weighting, and supports integration with state-of-the-art conditional
generative models such as the guided diffusion and conditional variational
autoencoder. We present a thorough evaluation of our method using both
synthetic and real-world data. Our results demonstrate that our method is
capable of generating high-quality counterfactual samples and outperforms the
state-of-the-art baselines.
