Gijs van Seeventer is currently developing methods for causal inference as part of his Ph.D. project at the Leiden Institute of Advanced Computer Science (LIACS). His research aims to contribute to an end-to-end framework for causal effect estimation, combining causal discovery, identification analysis, and experiment design. By integrating techniques from statistics, graphical model learning, and stochastic optimization, the project addresses the challenge of recovering causal structures from observational data and optimally selecting costly interventions.

Born in 1999 in Utrecht, the Netherlands, Gijs obtained a B.Sc. in Physics & Astronomy and a B.Sc. in Philosophy at Utrecht University, followed by an M.Sc. in Theoretical Physics with a focus on high-energy (particle) physics obtained in 2025. He is currently pursuing his Ph.D. under the supervision of dr. Matthijs van Leeuwen and dr. Saber Salehkaleybar.

His research interests include the foundations and methodology of causal inference, with inspiration drawn from both the philosophical roots of causality, and its practical applications in medicine, economics, and machine learning.