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stat.ML2026
Flow-based Generative Modeling of Potential Outcomes and Counterfactuals
Dongze Wu, David I. Inouye, Yao Xie
Predicting potential and counterfactual outcomes from observational data is central to individualized decision-making, particularly in clinical settings where treatment choices mus…
stat.ML2026
DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
Dongze Wu, Feng Qiu, Yao Xie
Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate…
stat.ML2025
Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions
Dongze Wu, Yao Xie
Sampling from high-dimensional, multi-modal distributions remains a fundamental challenge across domains such as statistical Bayesian inference and physics-based machine learning.…