用流模型统一建模潜在结果与反事实，实现个性化决策预测。
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
- 基于连续归一化流构建联合概率模型，同时学习真实结果与假设治疗下的反事实分布。
- 在基准数据集上实现高精度的个体化结果预测和处理效应估计，支持不确定性评估。
- 适合临床决策、个性化医疗等需精准反事实推断的场景。
从观测数据中预测潜在结果与反事实结果是个性化决策的核心，尤其在临床环境中，治疗选择需基于个体而非群体平均。我们提出PO-Flow，一种基于连续归一化流（CNF）的因果推断框架，联合建模潜在结果分布与基于真实结果的反事实结果。通过流匹配训练，PO-Flow实现了个体化潜在结果预测、条件平均处理效应估计与反事实预测的统一方法。通过编码真实观测结果并解码至替代治疗下，构建了事实条件的反事实生成机制。此外，该方法支持基于似然的潜在结果评估，实现预测的不确定性感知。在特定假设下建立了支持性恢复保证，基准数据集上的实证结果表明其在潜在结果框架下的多种因果推断任务中表现优异。
原文摘要 · Abstract (English)
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.
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