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.
@article{arxiv.2309.13378,
title = {Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment},
author = {Yutong Xia and Yuxuan Liang and Haomin Wen and Xu Liu and Kun Wang and Zhengyang Zhou and Roger Zimmermann},
journal= {arXiv preprint arXiv:2309.13378},
year = {2023}
}