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

2024-09-29

Neural Information Processing Systems

AI Summary

To address three key challenges in spatiotemporal forecasting—data scarcity, absence of causal structure, and poor generalizability—this paper proposes CaPaint, a two-stage causal framework. First, it performs spatiotemporal causal discovery to identify causal regions within the data; second, it applies diffusion-based image inpainting on non-causal subregions, guided by backdoor adjustment to reliably infer the underlying distribution. Key contributions include: (i) the first integration of causal discovery with diffusion-based inpainting; (ii) a novel backdoor-adjustment-driven causal masking and inpainting mechanism; and (iii) reduction of spatiotemporal causal discovery complexity from exponential to quasi-linear. Evaluated on five real-world spatiotemporal benchmarks, CaPaint achieves average performance gains of 4.3%–77.3% over state-of-the-art baselines, significantly outperforming conventional data augmentation methods. These results validate the effectiveness of the new “causal-guided + generative inpainting” paradigm for robust spatiotemporal modeling.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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 causal framework for ST predictions, 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 utilize the back-door adjustment to specifically address the sub-regions identified as non-causal in the upstream phase. Specifically, we employ a novel image inpainting technique. By using a fine-tuned unconditional Diffusion Probabilistic Model (DDPM) as the generative prior, we in-fill the masks defined as environmental parts, offering the possibility of reliable extrapolation for potential data distributions. CaPaint overcomes the high complexity dilemma of optimal ST causal discovery models by reducing the data generation complexity from exponential to quasi-linear levels. Extensive experiments conducted on five real-world ST benchmarks demonstrate that integrating the CaPaint concept allows models to achieve improvements ranging from 4.3% to 77.3%. Moreover, compared to traditional mainstream ST augmenters, CaPaint underscores the potential of diffusion models in ST enhancement, offering a novel paradigm for this field. Our project is available at https://anonymous.4open.science/r/12345-DFCC.

Problem

• Addresses data scarcity and imbalance in spatio-temporal prediction.
• Enhances model generalizability and interpretability through causal reasoning.
• Reduces data generation complexity from exponential to quasi-linear levels.

Innovation

• Causal framework enhances ST prediction interpretability.
• Diffusion model inpainting fills environmental data gaps.
• Reduces data generation complexity to quasi-linear levels.