Demonstrating Nexus for Correlation Discovery over Collections of Spatio-Temporal Tabular Data

Causal analysis is crucial for understanding cause-and-effect relationships in observed data to inform better decisions. However, conducting precise causal analysis on observational data is usually impractical, so domain experts often begin their exploration by identifying correlations. In this paper, we demonstrate Nexus, a system that aligns tabular datasets across space and time, handles missing data, and identifies correlations deemed "interesting", facilitating the exploration of causal relationships.