Causal Deciphering and Inpainting in Spatio-Temporal Dynamics via Diffusion Model

Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception. However, the scarcity of data coupled with the high expenses involved in sensor deployment results in notable data imbalances. Furthermore, models that are excessively customized and devoid of causal connections further undermine the generalizability and interpretability. To this end, we establish a framework for ST predictions from a causal perspective, termed CaPaint, which targets to identify causal regions in data and endow model with causal reasoning ability in a two-stage process. Going beyond this process, we build on the front door adjustment as the theoretical foundation to specifically address the sub-regions identified as non-causal in the upstream phase.