"I hope this research will help scientists in a variety of disciplines to discover new and valid causal relations" - Jakob Raymaekers
Jakob Raymaekers, Assistant Professor at the deparment of Quantitative Economics (MSCM)
Robust Causal DiscoveryCan we learn causal mechanisms from observational data? The answer is yes, under the right conditions. While some of these conditions are known, they are not completely understood. All too often, we assume that the data we observe is free from measurement errors and anomalies. Jakob aims to eliminate this assumption and explore how far we can push the boundaries of causal discovery. More precisely, he will investigate whether causal discovery is still possible when the data is not so well-behaved and thus more realistic.
With his research, Jakob aims to advance the theoretical understanding of causal discovery and build a versatile toolbox to support scientists making causal discoveries and improve the reliability of their findings.