Counterfactual-Augmented Representation Learning based Event Prediction

Accurate prediction of future events holds significant importance for decision-makers. Current methods learn representations of past events from the observable graph structure to predict whether a future event will occur. However, these methods overlook the counterfactual scenarios, thus missing essential factors that could trigger future events. In this paper, we propose the Predicting Events with Counterfactual Augmentation Framework (PECF) to address this limitation. This is achieved by investigating whether deviations from observed events (i.e., counterfactual events) can affect the occurrence of the target future event. Specifically, first, we learn the representations of events through the temporal event graphs. Then, we instantiate causal models to represent the causal relationships between events. Finally, we generate counterfactual events and enhance event prediction accuracy through counterfactual-based augmentation. Experimental results demonstrate that our method outperforms current state-of-the-art methods on benchmark datasets. The code is available at https://github.com/hucheng-IIE/PECF.