On Experiments for Causal Inference and System Identification

Understanding the Nature of Inference: Correlation and Causation Colloquium Series
Nihat Ay, Max Planck Institute for Mathematics in the Sciences

Nihat Ay will provide an introduction to the field of causal networks. He will focus on instructive simple examples in order to highlight the core conceptual and philosophical ideas that underlie the theory. This approach provides a clear semantics that allows us to distinguish between causation and correlation. It also allows us to study how cause-effect relations give rise to particular kinds of correlations, which can then be exploited for inferring causal relations from correlations. However, this kind of inference, which is based on observations only, turns out to be rather limited. In addition to observing a system, one can also apply experimental interventions and thereby reveal causal structures that would not follow from correlational information.  This is one instance of system identification through experimental perturbations. He will conclude his presentation with an extension of the interventional calculus by a “knockout calculus” which is based on a kind of structural intervention. This allows us to reveal a more fine-grained causal description of the underlying system.    

Sponsored by the Franke Program in Science and the Humanities, John Templeton Foundation
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Poster

3:00 pm, Zoom EST