Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment

Spatio-Temporal Graph Neural Networks 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.

Paper, Code

Citation:

@article{xia2023deciphering,
  title={Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment},
  author={Xia, Yutong and Liang, Yuxuan and Wen, Haomin and Liu, Xu and Wang, Kun and Zhou, Zhengyang and Zimmermann, Roger},
  journal={arXiv preprint arXiv:2309.13378},
  year={2023}
}