STOAT: Spatial-Temporal Probabilistic Causal Inference Network
This work addresses the challenge of accurate and calibrated uncertainty modeling for spatial-temporal forecasting, which is crucial for applications like epidemic management, though it appears incremental as it builds on existing causal and probabilistic methods.
The paper tackled the problem of probabilistic forecasting in spatial-temporal causal time series by proposing STOAT, a framework that integrates spatial dependencies and causal inference, and it outperformed state-of-the-art models like DeepAR on COVID-19 data across six countries, showing improved metrics especially in regions with strong spatial dependencies.
Spatial-temporal causal time series (STC-TS) involve region-specific temporal observations driven by causally relevant covariates and interconnected across geographic or network-based spaces. Existing methods often model spatial and temporal dynamics independently and overlook causality-driven probabilistic forecasting, limiting their predictive power. To address this, we propose STOAT (Spatial-Temporal Probabilistic Causal Inference Network), a novel framework for probabilistic forecasting in STC-TS. The proposed method extends a causal inference approach by incorporating a spatial relation matrix that encodes interregional dependencies (e.g. proximity or connectivity), enabling spatially informed causal effect estimation. The resulting latent series are processed by deep probabilistic models to estimate the parameters of the distributions, enabling calibrated uncertainty modeling. We further explore multiple output distributions (e.g., Gaussian, Student's-$t$, Laplace) to capture region-specific variability. Experiments on COVID-19 data across six countries demonstrate that STOAT outperforms state-of-the-art probabilistic forecasting models (DeepAR, DeepVAR, Deep State Space Model, etc.) in key metrics, particularly in regions with strong spatial dependencies. By bridging causal inference and geospatial probabilistic forecasting, STOAT offers a generalizable framework for complex spatial-temporal tasks, such as epidemic management.