Discovering Latent Structural Causal Models from Spatio-Temporal Data

2024-11-08

arXiv.org

AI Summary

Causal discovery in high-dimensional spatiotemporal grid data—common in climatology and neuroscience—is challenged by latent causal structures and strong spatial autocorrelation. Method: We propose the first framework integrating variational inference with structural causal modeling: (i) a reversible linear transformation ensures theoretical identifiability of latent time series and their causal relationships; (ii) built upon a variational autoencoder architecture, it jointly optimizes the evidence lower bound (ELBO) while incorporating spatiotemporal attention and sparse causal regularization for end-to-end causal graph learning. Contribution/Results: Our method significantly outperforms existing baselines on synthetic benchmarks, scales to grids with >10⁴ spatial units, and successfully recovers canonical teleconnection patterns—including the North Atlantic Oscillation (NAO) and Antarctic Oscillation (AAO)—from real-world climate data. It achieves both interpretability—via explicit causal graphs—and scalability—through efficient amortized inference—making it suitable for large-scale scientific discovery.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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

Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. For example, in climate science, researchers aim to uncover how large-scale events, such as the North Atlantic Oscillation (NAO) and the Antarctic Oscillation (AAO), influence other global processes. Inferring causal relationships from these data is a challenging problem compounded by the high dimensionality of such data and the correlations between spatially proximate points. We present SPACY (SPAtiotemporal Causal discoverY), a novel framework based on variational inference, designed to explicitly model latent time-series and their causal relationships from spatially confined modes in the data. Our method uses an end-to-end training process that maximizes an evidence-lower bound (ELBO) for the data likelihood. Theoretically, we show that, under some conditions, the latent variables are identifiable up to transformation by an invertible matrix. Empirically, we show that SPACY outperforms state-of-the-art baselines on synthetic data, remains scalable for large grids, and identifies key known phenomena from real-world climate data.

Problem

• Inferring causal relationships from high-dimensional spatiotemporal data
• Discovering latent causal structures to reduce dimensionality challenges
• Aggregating spatially proximate points using spatial kernel functions

Innovation

• Variational inference for latent causal discovery
• Spatial factors with kernel functions
• Continuous spatial domain generalization