ERC Advanced Grant · 2024
Unifying Graph Databases and Causal Models
Graphs are expressive data structures representing more effectively relationships in data and enabling complex data-intensive tasks. Graphs are also a widely adopted paradigm in causal inference focusing on causal directed acyclic graphs. Causal DAGs (Directed Acyclic Graphs) are manually curated by domain experts, but they are not validated, stored, integrated and versioned as data artifacts in a graph data system. In the GOY (pronounced GO Why) project, I focus on a radical shift towards causality-driven graph databases, addressing the development of solid theoretical foundations and a set of adequate tools underpinning causal graph operations. To date, these two paradigms, namely…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.