M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data

• URL: http://arxiv.org/abs/2605.00398v1
• Date: Fri, 01 May 2026 04:40:35 GMT
• Title: M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
• Abstract summary: Causal graph discovery for space-time systems is challenging in high-dimensional gridded data.<n>CaStLe was developed to address that niche under space-time locality and stationarity assumptions.<n>We present M-CaStLe to jointly model local within-identification and cross-variable space-time causal structures in gridded data.
• Score: 0.6054759773528756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observations per cell. The Causal Space-Time Stencil Learning (CaStLe) meta-algorithm was developed to address that niche under space-time locality and stationarity assumptions, but it is currently limited to univariate analyses. In this work, we present M-CaStLe. M-CaStLe generalizes the local embedding and parent-identification phases of CaStLe to jointly model local within-variable and cross-variable space-time causal structures in gridded data. Like CaStLe, by constraining candidate parents to a constant-size space-time neighborhood and pooling spatial replicates, M-CaStLe increases effective sample size to make discovery tractable in high-dimensional settings. We further decompose the resulting multivariate stencil graph into reaction and spatial graphs to aid interpretation in complex settings. We study M-CaStLe in four settings: a multivariate space-time vector autoregression benchmark with known ground truth, an advective-diffusive-reaction partial differential equation verification problem with derived physical reference structure, an atmospheric chemistry case study in a low-temporal-sample regime, and an El Niño Southern Oscillation study on reanalysis data, identifying phase-dependent ocean--atmosphere coupling. Across these settings, M-CaStLe more accurately recovers multivariate causal structure in controlled settings and identifies important physical dynamics in real-world case studies. Overall, M-CaStLe advances causal discovery for multivariate space-time systems while retaining interpretability at the grid level.

Related papers

• CASC: Causal Adversarial Subspace Clustering for Multivariate Spatiotemporal Data [0.16206783799607727]
  We propose a novel Causal Adrial Subspace Clusteringgeneration (CASC) framework for discovering latent regimes in high-dimensional data.<n>CASC integrates a U-Net-inspired deep adversarial clustering architecture with stacked FAConvLSTM layers to preserve spatial and temporal structure.<n>We also propose two new learning objectives: (1) a Causal Preservation Loss that aligns self-expression with latent causal relationships, and (2) a Dynamic Temporal Subspace Evolution Loss that captures evolving subspace structures and temporal regime transitions in nonstationary environments.
  arXiv  Detail & Related papers  (2026-07-23T09:19:17Z)
• Causal Representation Meets Stochastic Modeling under Generic Geometry [49.24293444627916]
  We develop causal representation learning for continuous-time latent point processes.<n>We develop MUTATE, an identifiable variational autoencoder framework with a time-adaptive transition module.<n>Across simulated and empirical studies, we find that MUTATE can effectively answer scientific questions.
  arXiv  Detail & Related papers  (2026-02-04T20:40:53Z)
• Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Complex Catastrophic Slope Failure [5.622625734292367]
  Local Intrinsic Dimensionality (LID) has shown strong potential for identifying anomalies and outliers in high-dimensional data.<n>We focus on ground-monitored landslides and introduce a novel approach that jointly incorporates spatial and temporal information.
  arXiv  Detail & Related papers  (2026-01-07T04:29:05Z)
• Attention-Guided Deep Adversarial Temporal Subspace Clustering (A-DATSC) Model for multivariate spatiotemporal data [0.16206783799607727]
  Subspace clustering models are vital for applications such as snowmelt detection sea ice tracking.<n>A graph attention based self-expressive network captures local spatial relationships, global dependencies, and both short- and long-range preserves.<n> Experiments on three real-world datasets show that A-SCSC achieves substantially superior clustering performance compared to state-of-the-art deep subspace clustering models.
  arXiv  Detail & Related papers  (2025-10-20T18:38:26Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Discovering Latent Causal Graphs from Spatiotemporal Data [23.400027588427964]
  Inferring causal relationships from climate data is a challenging problem with high dimensionality.<n>We present SPACY (tiotemporal CausalY), a novel framework for inference based on latent causal structures.<n> SPACY also identifies key known phenomena from real-world climate data.
  arXiv  Detail & Related papers  (2024-11-08T05:12:16Z)
• IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion [97.58125811599383]
  Temporal Knowledge Graphs (TKGs) incorporate a temporal dimension, allowing for a precise capture of the evolution of knowledge. We propose a novel Multi-curvature shared and specific Embedding (IME) model for TKGC tasks. IME incorporates two key properties, namely space-shared property and space-specific property.
  arXiv  Detail & Related papers  (2024-03-28T23:31:25Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Variational Mode Decomposition-Based Nonstationary Coherent Structure Analysis for Spatiotemporal Data [0.0]
  This paper presents a variational mode decomposition (VMD-NCS) analysis that enables the extraction of coherent structures in the case of nonstationary phenomena. Unlike many conventional modal analysis techniques, the proposed method accounts for the temporal changes in the spatial distribution with time.
  arXiv  Detail & Related papers  (2023-12-19T12:36:39Z)
• Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection [67.60791405198063]
  We propose a correlation-aware spatial-temporal graph learning (termed CST-GL) for time series anomaly detection. CST-GL explicitly captures the pairwise correlations via a multivariate time series correlation learning module. A novel anomaly scoring component is further integrated into CST-GL to estimate the degree of an anomaly in a purely unsupervised manner.
  arXiv  Detail & Related papers  (2023-07-17T11:04:27Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.