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

• URL: http://arxiv.org/abs/2309.13378v1
• Date: Sat, 23 Sep 2023 13:51:09 GMT
• Title: Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment
• Authors: Yutong Xia, Yuxuan Liang, Haomin Wen, Xu Liu, Kun Wang, Zhengyang Zhou, Roger Zimmermann
• Abstract summary: We propose a novel framework called CaST to tackle temporal out-of-distribution issues and dynamic spatial causation. Experiments results on three real-world datasets demonstrate the effectiveness and practicality of CaST.
• Score: 33.4989883914555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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