Learning to make trustworthy decisions: predictive models, assumption checking or experiments?

You can join online through the following link https://chalmers.zoom.us/j/69140014938 pw:monday

Abstract

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.