Discovering Latent Structural Causal Models from Spatio-Temporal Data

Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yian Ma, Rose Yu

Keywords: Causal Inference, Causal Represetation Learning, Spatio-Temporal Dynamic Modeling, Variational Inference, Probablistic Graphical Model

TL;DR: We introduce a novel framework for causal discovery from high-dimensional spatio-temporal data.

Abstract:

Many important phenomenon in scientific fields such as climate, neuroscience and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Inferring causal relationships from these data is a difficult 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.

Primary Area: causal reasoning

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Submission Number: 5760