DoFlow is a novel generative model specifically developed for time-series forecasting, with a focus on both observational and causal predictions. It employs a causal directed acyclic graph (DAG) framework, enabling it to handle complex scenarios such as interventional and counterfactual queries. This methodological approach allows DoFlow to provide accurate forecasts across various conditions, marking a significant advancement in the field of time-series analysis. The model’s design supports enhanced understanding and prediction of causal relationships within temporal data. According to recent claims, DoFlow represents an innovative step forward and demonstrates improved forecast accuracy. Its capabilities position it as a valuable tool for applications requiring nuanced causal inference in time-dependent contexts. This development aligns with ongoing research efforts to integrate causality into machine learning models for more robust and interpretable predictions.