PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals

• URL: http://arxiv.org/abs/2505.16051v2
• Date: Thu, 09 Oct 2025 02:28:01 GMT
• Title: PO-Flow: Flow-based Generative Models for Sampling Potential Outcomes and Counterfactuals
• Authors: Dongze Wu, David I. Inouye, Yao Xie,
• Abstract summary: We propose PO-Flow, a continuous normalizing flow (CNF) framework for causal inference that jointly models potential outcomes and counterfactuals.<n>PO-Flow provides a unified approach to average treatment effect estimation, individualized potential outcome prediction, and counterfactual prediction.
• Score: 20.10830878838357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting potential and counterfactual outcomes from observational data is central to clinical decision-making, where physicians must weigh treatments for an individual patient rather than relying solely on average effects at the population level. We propose PO-Flow, a continuous normalizing flow (CNF) framework for causal inference that jointly models potential outcomes and counterfactuals. Trained via flow matching, PO-Flow provides a unified approach to average treatment effect estimation, individualized potential outcome prediction, and counterfactual prediction. Besides, PO-Flow directly learns the densities of potential outcomes, enabling likelihood-based evaluation of predictions. Furthermore, PO-Flow explores counterfactual outcome generation conditioned on the observed factual in general observational datasets, with a supporting recovery result under certain assumptions. PO-Flow outperforms modern baselines across diverse datasets and causal tasks in the potential outcomes framework.

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