Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and
  Treatment
Yutong Xia Yuxuan Liang Haomin Wen Xu Liu Kun Wang Zhengyang Zhou Roger Zimmermann
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.