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Learning to make trustworthy decisions: predictive models, assumption checking or experiments?
In many domains of science and society we are interested in making good decisions. For instance, should we use drug A or drug B to treat a disease, will this advertisement lead to higher spending, or will this new education intervention lead to better learning outcomes? Statistical machine learning does not directly address these questions, but perhaps we can use it as part of a solution to do better, more trustworthy causal inference to answer such questions. In this talk, I will cover various building blocks my research group investigates to address this, starting with a discussion of the inadequacies of prediction, to the difficulties of causal assumption checking and the challenges of personalized decision support using experimental data. We end with some current open problems and future directions of investigation that our “Safe Causal Inference” consortium will work on in the coming years.
About the speaker
Jesse Krijthe is an assistant professor in machine learning at TU Delft. His research concerns the development, study and application of statistical and causal machine learning methods, in particular for decision support in the medical domain. He combines work on methodological contributions in statistical machine learning with applications of these methods to data analysis in various domains, ranging from Parkinson’s disease and cognitive health to osteoarthritis and intensive care medicine.
The Causal Analysis of Biomedical Data Lecture Series is supported by the Luxembourg National Research Fund (FNR) RESCOM Program.
