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.

翻译：估计平均因果效应是检验新处理方法的常见做法。然而，平均效应“掩盖”了反事实分布中重要的个体特征，这可能导致安全性、公平性和伦理问题。在时序设定中，当处理是序贯且时变时，这一问题更为严重，导致对反事实分布产生复杂影响。本文提出一种新型条件生成建模方法，用于捕捉完整的反事实分布，从而能够对反事实分布的特定统计量进行高效推断。这使得所提方法特别适用于医疗保健和公共政策制定。我们的生成建模方法通过边际结构模型，细致地解决了观测数据与目标反事实分布之间的分布失配问题。该方法在合成数据和真实数据上均优于现有最优基线模型。