Code from: SECT: A spatiotemporal explicit causal transformer for path-faithful spatiotemporal attribution

This package gives the implementation of the Spatiotemporal Explicit
Causal Transformer (SECT), a dual-stream architecture that encodes self
and neighbor sequences with causal convolutions and temporal transformers,
integrates spatial self-information (SSI) as a localized anomaly metric,
and fuses the streams with attention-based pooling. Interpretability is
achieved through layer-wise relevance propagation with conservation
checks, ensuring attribution is preserved and comparable at the
neighbor–variable–lag (NVL) level. In a winter storm outage case study,
SECT achieves competitive predictive performance relative to strong
temporal and spatial baselines while preserving explicit neighbor identity
and lag structure. Multi-seed ablation, knockout, and falsification
experiments demonstrate that the model's performance degrades
systematically when temporal order, neighbor alignment, or anomaly
structure are perturbed, supporting the structural validity of its learned
pathways. These results position SECT as a framework that integrates
predictive modeling with experimentally testable, pathway-level
spatiotemporal attribution.