Inaugural lectures by Peters and Adiprasito
13:30: Jonas Peters: "Causal Data Science"
14:15: Karim Adiprasito: "From discrete to continuous and back"
15:00 Reception in front of Auditorium 1
Causal Data Science
Abstract: Causality enters data science in different ways. Often, we are interested in knowing how a system reacts under a specific intervention, e.g., when considering gene knock-outs or a change of policy. The goal of causal discovery is to learn causal relationships from data. Other practical problems in data science focus on prediction. But as soon as we want to predict in a scenario that differs from the one which generated the available data (we may think about a different country or experiment), it might still be beneficial to apply causality related ideas. We present assumptions, under which causal structure becomes identifiable from data and methods that are robust under distributional shifts. We further present several challenges in data science that may be addressed in future research. No knowledge of causality is required.
From discrete to continuous and back
Abstract: I will discuss some of the curious cases when discrete objects behave like the continuous, and some when continuous behave like the discrete, and how that helps us understand both a little better.