Title: DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Time Series, Causal Inference, Generative Models, Flow Matching

Abstract: Time-series forecasting increasingly demands not only accurate observational predictions but also causal forecasting under interventional and counterfactual queries in multivariate systems. We present DoFlow, a flow-based generative model defined over a causal DAG that delivers coherent observational and interventional forecasts, as well as counterfactuals through the natural encoding–decoding mechanism of continuous normalizing flows (CNFs). We also provide a supporting recovery result under certain assumptions.  Beyond forecasting, DoFlow provides explicit likelihoods of future trajectories, enabling principled change-point and anomaly detection. Experiments on synthetic datasets with various causal DAG structures and real-world hydropower time series show that DoFlow achieves accurate system-wide observational forecasting, enables causal forecasting over interventional and counterfactual queries, and effectively detects anomalies.