## 2 Literature Review
### Evaluating Incident Effects on Urban Rail Transit Systems  
[PERSON], et al. [24] developed a predictive model originally for regular operational scenarios that has been adapted as counterfactual tools. These models predict expected passenger flows under normal conditions, providing a baseline against which actual passenger flows during incidents and emergencies are compared, effectively quantifying incidents' impacts [25]. Additionally, [PERSON], et al. [9] developed an OD prediction framework explicitly incorporating incidents as variables, enabling comparisons between normal and disrupted conditions and effectively quantifying passenger flow deviations attributable to disruptions. Other targeted models directly predict passenger flow during incidents and emergencies, contrasting them against baseline scenarios to determine impacts [26; 27]. Simulation methods further support these analyses, providing estimates of passenger flows at stations experiencing disruptions, thus illuminating detailed incident impacts [28]. Causal inference methods have also been applied to operational data to systematically evaluate subway disruption effects on passenger flows, notably highlighting spillover phenomena [29].  
In practical applications, several urban rail systems have implemented real-time OD prediction models to enhance operational resilience during incidents. For instance, the Hong Kong Mass Transit Railway (MTR) has adopted a multi-resolution spatiotemporal deep learning model to forecast OD demand, facilitating dynamic control strategies and passenger information dissemination during unexpected events [30]. These implementations underscore the practical utility of advanced OD prediction models in real-world urban rail operations.  
Although significant progress has been made in the prediction of passenger origin-destination (OD) matrices, existing methods ranging from classical models to deep learning (DL) and causal inference-based approaches still face substantial limitations, particularly in the context of operational incidents. Classical methods often simplify the problem by assuming structural regularity or low-rank properties, but these approaches struggle to handle the high-dimensional and dynamic nature of the data. DL models have made strides in capturing nonlinear spatiotemporal dependencies, yet they often lack interpretability, hindering their practical application in emergency scenarios. While causal inference-based methods offer valuable insights into system resilience, they are limited by a focus on general operational conditions, with insufficient exploration of real-time incident impacts. The proposed framework in this study bridges these gaps by integrating deep counterfactual inference, which enhances both prediction accuracy and interpretability. The motivation behind our approach stems from the need to predict OD matrices in real-time during incidents, a scenario largely unexplored by existing methods. By using KL divergence-based propensity matching and a dual-channel architecture, our model captures the spatiotemporal impacts of disruptions and provides interpretable insights into the underlying mechanisms.