## 1 Introduction  
With the rapid growth of urban rail networks, the volume and complexity of passenger flows have substantially increased, posing significant management challenges, especially under unplanned incidents and emergencies. Incident-induced disruptions, including equipment incidents, infrastructure damage, natural disasters, and human-related incidents, severely impact urban rail efficiency and reliability [1]. Common subway incidents, such as power equipment failures, trackside equipment faults, signal disruptions, and train malfunctions (see Figure 1), result in varying degrees of service interruption, thereby affecting passenger flow. However, existing research lacks timely and accurate predictions of passenger origin-destination (OD) flows specifically under disrupted conditions, exacerbating congestion and causing cascading impacts across transit networks [2]. Addressing this gap by accurately predicting OD matrices during incidents is critical for enhancing operational resilience and proactive incident management [3,4]. Recently, advanced deep learning (DL) techniques have demonstrated promising results in enhancing passenger OD matrix predictions due to their capability to model complex nonlinear relationships and high-dimensional spatiotemporal data [5,6]. Techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and even Transformers have shown substantial improvements over traditional models [5,7,8]. However, despite prediction methodological advancements, conventional DL models often struggle with interpretability issues, providing limited insights into the underlying mechanisms driving passenger flow variations during incidents. The lack of interpretability hampers their practical application in operational decision-making scenarios, where understanding the causal relationships and spatiotemporal impacts of disruptions is crucial [9]. Furthermore, due to the scarcity of incident-specific data, it is challenging to simultaneously observe changes in OD matrices under both incident and normal conditions, complicating accurate predictions and causal analysis. Figure 1: Common types of urban rail transit system incidents. Therefore, to address these research gaps, the objective of this study was to develop an interpretable deep counterfactual inference framework to accurately predict passenger OD matrices under operational disruptions in urban rail systems. Counterfactual inference offers a robust analytical approach for capturing causal relationships and providing interpretable predictions through \"what-if\" scenario estimation [10; 11]. Specifically, we first designed a tensor decomposition model coupled with the Kullback-Leibler (KL) divergence measure to effectively represent and quantify passenger flow dynamics, facilitating robust propensity matching between normal and disrupted conditions. Then, we develop a dual-channel deep counterfactual prediction model based on multi-task learning paradigms, which simultaneously estimates passenger OD matrices under normal operational conditions and counterfactually predicts passenger OD flows under incident-induced disruptions. By quantifying the divergence between the normal-condition and incident-related passenger flows using tensor-based metrics, the proposed framework explicitly models the spatiotemporal dynamics and causal impacts of disruptions, providing interpretable predictions and actionable insights. Empirical analyses conducted using real-world data from Shanghai's extensive urban rail network demonstrate significant improvements in prediction accuracy under incident conditions compared to baseline models, with Mean Absolute Percentage Error (MAPE) reductions ranging from 40% to 70%, thereby confirming the practical effectiveness of the proposed approach. Our study contributes to the existing knowledge in three aspects.