About the project
Understanding the behaviour of a system under the influence of interventions is the ultimate goal of many scientific studies. While such causal relationships are ideally inferred from interventional data obtained through controlled experiments, one has often only access to data that has been obtained by passively observing the system. Hence, there has lately been a growing interest in the development of machine learning methods for inferring causal relationships from observational data given certain causal assumptions. In this project we will develop causal discovery methods based on the framework of causal graphical models by combining structure learning and causal calculus. In particular, we will focus on a Bayesian approach to this problem in order to better account for the uncertainty in the inference procedure.
Project team
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PhD Fellow Vera Kvisgaard, University of Oslo, Department of Mathematics
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Associate Professor Johan Pensar, University of Oslo, Department of Mathematics