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Machine learning for causal inference

Konrad Kording, University of Pennsylvania
Tuesday January 9, 12-1pm
Zoom Link:听
In Person: 550 Sherbrooke, Room 189

Abstract:听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鈥檚 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.