DoFlow: Flow-based Generative Models for Interventional and Counterfactual Forecasting on Time Series
DoFlow:用于时间序列干预与反事实预测的流生成模型
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 Directed Acyclic Graph (DAG) that delivers coherent observational and interventional predictions, as well as counterfactuals through the natural encoding-decoding mechanism of continuous normalizing flows (CNFs). We…