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Flow-based Generative Modeling of Potential Outcomes and Counterfactuals

About

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.

Dongze Wu, David I. Inouye, Yao Xie• 2025

Related benchmarks

TaskDatasetResultRank
Individual Treatment Effect EstimationIHDP (out-of-sample)--
45
CATE estimationACIC 2016 (Out-sample)
PEHE0.82
22
CATE estimationIHDP (In-sample)
PEHE0.41
22
CATE estimationIHDP (Out-sample)
PEHE0.45
22
Potential Outcome EstimationACIC in-sample 2018
RMSE0.04
19
Potential Outcome EstimationACIC out-of-sample 2018
RMSE0.05
19
Potential Outcome EstimationIHDP (In-sample)
RMSE0.96
19
Potential Outcome EstimationIBM in-sample
RMSE0.04
19
Potential Outcome EstimationIBM out-of-sample
RMSE0.04
19
CATE estimationACIC 2016 (In-sample)--
13
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