Estimating average causal effects is a common practice to test new
treatments. However, the average effect ''masks'' important individual
characteristics in the counterfactual distribution, which may lead to safety,
fairness, and ethical concerns. This issue is exacerbated in the temporal
setting, where the treatment is sequential and time-varying, leading to an
intricate influence on the counterfactual distribution. In this paper, we
propose a novel conditional generative modeling approach to capture the whole
counterfactual distribution, allowing efficient inference on certain statistics
of the counterfactual distribution. This makes the proposed approach
particularly suitable for healthcare and public policy making. Our generative
modeling approach carefully tackles the distribution mismatch in the observed
data and the targeted counterfactual distribution via a marginal structural
model. Our method outperforms state-of-the-art baselines on both synthetic and
real data.

本文提出了一种基于条件生成建模的方法，旨在捕捉整个反事实分布，使其特别适用于医疗保健和公共政策制定。通过边际结构模型来解决观察数据和目标反事实分布之间的分布不匹配问题，该方法在合成和真实数据上优于现有方法。