Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular method for STG forecasting, but they often struggle with temporal out-of-distribution (OoD) issues and dynamic spatial causation. In this paper, we propose a novel framework called CaST to tackle these two challenges via causal treatments. Concretely, leveraging a causal lens, we first build a structural causal model to decipher the data generation process of STGs. To handle the temporal OoD issue, we employ the back-door adjustment by a novel disentanglement block to separate invariant parts and temporal environments from input data. Moreover, we utilize the front-door adjustment and adopt the Hodge-Laplacian operator for edge-level convolution to model the ripple effect of causation. Experiments results on three real-world datasets demonstrate the effectiveness and practicality of CaST, which consistently outperforms existing methods with good interpretability.

通过使用因果治疗，本文提出了一种名为CaST的新框架来解决时空图预测中的时间分布外问题和动态空间因果问题，该框架通过建立结构因果模型来解析STG的数据生成过程，使用新的解缠结块通过反门调整来处理时间分布外问题，并利用前门调整和Hodge-Laplacian操作符进行边级卷积，以模拟因果效应的涟漪效果。在三个真实数据集上的实验证明了CaST的有效性和实用性，并且在解释性方面表现出色。

解读时空图预测：因果视角与处理