Discovering causal relations from data lies at the heart of most scientific research today. In apparent contradiction with the adagio “correlation does not imply causation”, recent theoretical insights indicate that such causal knowledge can also be derived from purely observational data, instead of only from controlled experimentation. In the “big data” era, such observational data is abundant and being able to actually derive causal relationships would open up a wealth of opportunities for improving science, healthcare, and possibly predictive maintenance. In this talk, I will sketch how insights from statistics and machine learning may lead to novel approaches for robust discovery of relevant causal relationships.
Stella Kapodistria is assistant professor in the Department of Mathematics and Computer Science, where she is part of the Stochastics operations section. She participates in the Networks Gravitation (NWO-Zwaartekracht) project, an NWO funded initiative to build self-organizing and intelligent networks by using algorithms and stochastics. She is also part of the DeSIRE research program on resilient engineering funded by the 4TU-call “High Tech for a Sustainable Future” and she is in the think-tank of the newly established 4TU center on Resilience Engineering. She is a co-applicant in the PrimaVera (NWA-ORC) project and the Real-time data-driven maintenance logistics (NWO – “Big Data: real time ICT for logistics”) project. As her main aim in her scientific work Stella states “revolutionizing our thinking and methods to solve today’s problems”. You can check out Stella’s scientific work here and here.