A gentle introduction to causal modeling

Abstract:

A fundamental goal of scientific inquiry is to gain a causal understanding of the phenomena under investigation, yet capturing causality in appropriate models is often hard. In this talk, we will discuss the fundamental principles of causal modeling, and what distinguishes causal models from associational ones. We will also have a closer look at the challenges that practitioners face when transferring causal techniques into applications, and we will review recent developments in the field.

About Jonas:

Jonas Wahl heads the research group Causal Models and Representations (CaMore) within the research department Neuro-mechanistic Modeling at the German Research Centre for Artificial Intelligence (DFKI). He received a PhD in Mathematics from KU Leuven. His research investigates how to gain and represent causal insights from data with a particular focus on the evaluation of causal structure methods and on causal learning from time series.