Jonas Peters
Associate Professor in Statistics
Department of Mathematical Sciences, University of Copenhagen
Universitetsparken 5, 2100 Copenhagen O, Denmark
eMail: firstname.lastname@math.ku.dk
Keywords: causality, computational statistics, machine learning, robustness, independence testing.
My work focuses mainly on causal inference: we try to learn causal structures either from purely observational data or from a combination of observational and interventional data. We therefore develop both theory and methodology. Our work relates to areas like high-dimensional statistics, computational statistics or graphical models. It's an exciting research area with lots of open questions!
Most of the publications are also on Google Scholar.
Even, if there is no PhD call and you think about doing a PhD or postdoc in causality, please send me an email.
We have written a book on causality that has appeared as open access at MIT Press. In July 2018, it was awarded the ASA causality in statistics education award.
Jonas Peters, Dominik Janzing, Bernhard Schölkopf: Elements of Causal Inference: Foundations and Learning Algorithms
The pdf can be downloaded for free from the MIT Press website (look for "This is an open access title" on the left-hand side).
Jonas is associate professor at the Department of Mathematical Sciences at the University of Copenhagen, and 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 für Statistik, ETH Zurich. He studied Mathematics at the University of Heidelberg and the University of Cambridge and did his PhD with Dominik Janzing, Bernhard Schölkopf and Peter Bühlmann. Jonas has done research internships with Leon Bottou at Microsoft Research (WA, USA), Martin Wainwright at UC Berkeley (CA, USA) and Peter Spirtes at CMU (Pittsburgh, USA). Jonas is interested in inferring causal relationships from observational data and works both on theory and methodology. His work relates to areas as computational statistics, graphical models, independence testing or high-dimensional statistics.
A full CV can be found here.
Bernoulli Society, Danish Society for Theoretical Statistics, IMS, ISI (elected), Royal Statistical Society
If we want to be able to describe the above situation properly, we need a so-called causal model that (1) models observational data and (2) interventional data (e.g., the distribution that arises after the gene deletion) and that (3) outputs a graph. Functional Causal Models (also called Structural Equation Models) are one class of such models, see the figure on the right. If you are interested in more details, see the script below, for example.
I have written a script on causality that I am more than happy to receive feedback on. Please note that it is still missing some sections. It can be downloaded here.
Kammermusikkreis Unterwachingen