The fields of machine learning and causality have each had remarkable progress through the last few decades. In recent years, there has also been an increasing interest into several different ways in which these fields may interact. On one hand, there is interest in how machine learning methods may be used to infer causal effects from observational data under various assumptions about the underlying data-generating process (causal structure learning, double machine learning etc). On the other hand, and in the opposite direction, there is interest into how the underlying causal structure may influence machine learning (causal versus anti-causal learning, implications for semi-supervised learning etc). Also, there is work into how observational (and/or experimental) data from heterogeneous environments may be fused, based on assumptions about the underlying causal structure, in order to successfully answer a query of interest.
We will start this breakfast meeting by giving a superficial overview of the various research directions that involve a combination of machine learning and causality, which we hope would be useful to a broad audience. After that, we will move over to a more in-depth discussion phase for those that have a particular interest in machine learning and causality, where the thematic focus would be tailored to the interests of those attending the discussion!
Program
08:45 – Breakfast is served
09:00 – Introduction by Geir Kjetil Sandve (Professor, Biomedical Informatics Research Group) and Johan Pensar (Associate Professor, Statistics and Data Science)
09:30 – Discussion
To participate, please fill out the registration form.
About the dScience Breakfast Club
In these breakfast meetings, researchers and others who work within the thematic field can meet and discuss a chosen topic. These meetings are open to everyone, but we ask you to sign up due to limited space. Breakfast will be served, accompanied with juice and coffee/tea. This is a monthly happening initiated by the dScience council.