In our NeurIPS 2023 paper “Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment”, we introduce CaST, a causal framework designed to tackle two key challenges in spatio-temporal graph forecasting: temporal distribution shifts and dynamic spatial causation. We are truly grateful …

Note: This blog is written based on my own understanding of concepts discussed in Weak Theory, Weak Modernism, Thinking Sideways: A Plea for “Weak Theory" and Weak Theory—A Report on the Contemporary. This blog is more like a self learning note summarizing key concepts and the original …

[The Github repo can be found here!]

This is a curated collection of papers on the intersection of causality (including causal inference and causal discovery), spatio-temporal data (including spatio-temporal graph/series data, grid data, and trajectory data), and machine learning. For clarity, …