AI RESEARCH
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
arXiv CS.LG
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ArXi:2505.16051v4 Announce Type: replace-cross 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.