Milestones from the project, and the workshop we’re planning to bring the graph data management and causality communities together.
Dazhuo Qiu, Yingli Zhou, Amedeo Pachera, Angela Bonifati and Andrea Mauri chart a causal data management ecosystem for decision making and agentic AI — a shared, persistent, queryable causal layer for trustworthy autonomous decisions. Read it on arXiv ↗
The project opens calls for a doctoral student in Data Management & Causality and a postdoctoral researcher in Temporal Causal Inference in Graph Databases. See open positions →
GO-Y begins its five-year run, hosted by the Database (BD) team at CNRS LIRIS, Université Claude Bernard Lyon 1, with an EU contribution of €2,315,565.
GO-Y secures European Research Council funding (Grant Agreement No. 101199575) to build the theoretical foundations of causality-driven graph data management.
The foundational result behind GO-Y, published at PVLDB 2025, demonstrates causal “what-if” analysis directly over graph databases — the first step toward causal query operators.
GO-Y will host a workshop bringing the graph data management and causality communities together to set a shared research agenda for causal graph databases. Date and venue to be announced.
An inaugural GO-Y workshop bringing the database, graph-systems and causality communities together — with keynotes, position talks and an open-problems panel to shape the agenda for causal graph databases.
▹ Planned · date & venue to be announcedCollaborate, attend the workshop, or join the team in Lyon as we advance causal graph data management.
