Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices must be tailored to each patient rather than guided solely by population averages. We propose PO-Flow, a continuous normalizing flow (CNF) framework for causal inference that jointly models potential outcome distributions and factual-conditioned counterfactual outcomes. Trained via flow matching, PO-Flow provides a unified approach to individualized potential outcome prediction, conditional average treatment effect estimation, and counterfactual prediction. By encoding an observed factual outcome and decoding under an alternative treatment, PO-Flow provides an encode-decode mechanism for factual-conditioned counterfactual prediction. In addition, PO-Flow supports likelihood-based evaluation of potential outcomes, enabling uncertainty-aware assessment of predictions. A supporting recovery guarantee is established under certain assumptions, and empirical results on benchmark datasets demonstrate strong performance across a range of causal inference tasks within the potential outcomes framework.

翻译：从观测数据预测潜在结果和反事实结果是个体化决策的核心，尤其在临床环境中，治疗方案必须针对每位患者量身定制，而非仅依据人群平均水平。我们提出PO-Flow，一种用于因果推断的连续归一化流（CNF）框架，能够联合建模潜在结果分布和基于事实条件的反事实结果。通过流匹配训练，PO-Flow为个体化潜在结果预测、条件平均处理效应估计和反事实预测提供了统一方法。通过编码观察到的事实结果并在替代处理下解码，PO-Flow实现了基于事实条件的反事实预测的编解码机制。此外，PO-Flow支持基于似然的潜在结果评估，从而实现预测的不确定性感知评估。在特定假设下，我们建立了支撑性的恢复保证，且基准数据集上的实证结果表明，在潜在结果框架内的各类因果推断任务中，该方法均展现出强劲性能。