Connections between causality and machine learning - Jonas Peters

Causal knowledge is required in order to predict a system’s response after an intervention. In this talk, we argue that machine learning methods can benefit from causal ideas in problems that go beyond predicting variables in interventional settings. Connections to systematic noise removal, reinforcement learning and domain adaptation exist but are not yet fully understood. We present applications in advertisement and exoplanet search. The talk covers joint work with numerous people, including B. Schoelkopf, D. Janzing, L. Bottou and M. Rojas-Carulla.
Bio
Jonas is an associate professor in statistics at the University of Copenhagen, he is a member of the “Junge Akademie”. Previously, Jonas has been leading the causality group at the MPI for Intelligent Systems in Tübingen and was a Marie Curie fellow at the Seminar for Statistics, ETH Zurich. He studied Mathematics in Heidelberg and Cambridge and did his PhD with B. Schölkopf, D. Janzing and P. Bühlmann, his thesis received the ETH medal. He has been working with L. Bottou at Microsoft Research Redmond (WA, USA), M. Wainwright at UC Berkeley (CA, USA) and P. Spirtes at CMU (PA, USA).

[@darthyzhu5767]

Great talk! wonder where to have a look at the slides.

[@zeta-man]

forget the slides, download the complete book from him on Causal Inference it's published as open access titles by MIT
mitpress.mit.edu/books/elements-causal-inference

[@bibekdahal3752]

This guy knows what he is talking. However, his audience there and me be like, wtf is this.