Discovering Latent Causal Graphs from Spatiotemporal Data

TL;DR

SPACY is a novel variational inference framework that uncovers latent causal structures in high-dimensional spatiotemporal data, effectively handling correlations and scaling to large datasets, with proven theoretical identifiability and superior empirical performance.

Contribution

The paper introduces SPACY, a new method for causal discovery in spatiotemporal data that models latent variables, generalizes to continuous space, and avoids unverifiable assumptions.

Findings

SPACY outperforms existing methods on synthetic data.

It successfully identifies known climate phenomena from real data.

Theoretical guarantees of identifiability are established.

Abstract

Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. 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 model latent time series and their causal relationships from spatiotemporal data. SPACY alleviates the high-dimensional challenge by discovering causal structures in the latent space. To aggregate spatially proximate, correlated grid points, we use spatial factors, parametrized by spatial kernel functions, to map observational time series to latent representations. Theoretically, we generalize the problem to a continuous spatial domain and establish identifiability when the observations arise from a nonlinear, invertible function of the product of latent series and spatial factors. Using this approach, we avoid assumptions that are often unverifiable, including those about instantaneous effects or sufficient variability. Empirically, SPACY outperforms state-of-the-art baselines on synthetic data, even in challenging settings where existing methods struggle, while remaining scalable for large grids. SPACY also identifies key known phenomena from real-world climate data. An implementation of SPACY is available at https://github.com/Rose-STL-Lab/SPACY/