ASSET Seminar: ML for Causal Inference (Konrad Körding, University of Pennsylvania)

Presentation Abstract: 

Machine learning traditionally does not get at causality and causality research traditionally treats machine learning as a dangerous set of highly biased estimators. In my talk I will talk about our lab’s efforts to use machine learning as a component of more traditional quasi-experimental techniques. I will also discuss meta-learning approaches to causal inference, approaches where the estimators themselves are learned. I will lament the relative lack of interactions between the various subfields of the causal inference space.

Speaker Bio:

Konrad Kording, PhD is a Professor of Neuroscience at the Perelman School of Medicine. Dr. Kording’s research has one single focus: data that matters. Early research in the lab focused on computational neuroscience and movement. But as the approaches matured, the focus has been on discovering ways in which new data sources and emerging data analysis can enable awesome possibilities. The current focus is on causality in data science applications. How do we know how things work if we can not randomize?