Discovering Latent Structural Causal Models from Spatio-Temporal Data

• URL: http://arxiv.org/abs/2411.05331v1
• Date: Fri, 08 Nov 2024 05:12:16 GMT
• Title: Discovering Latent Structural Causal Models from Spatio-Temporal Data
• Authors: Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yi-An Ma, Rose Yu,
• Abstract summary: We present SPACY (SPAtiotemporal Causal discoverY), a novel framework based on variational inference. 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.
• Score: 23.400027588427964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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