Jonas Peters
Associate Professor in Statistics
Department of Mathematical Sciences, University of Copenhagen
Universitetsparken 5, 2100 Copenhagen O, Denmark


Keywords: causality, computational statistics, machine learning, 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.



Causality book

We have written a book on causality that is now being copy-edited and that will appear as open access at MIT Press. A first draft is now available for download.

Jonas Peters, Dominik Janzing, Bernhard Schölkopf: Elements of Causal Inference: Foundations and Learning Algorithms






Open Positions

If you think about doing a PhD in causality, please send me an email.




Jonas is associate professor at the Department of Mathematical Sciences at the University of Copenhagen. He is a member of the Junge Akademie.

Until August 2016, Jonas was a group leader for the causality group at the Empirical Inference group at MPI Tübingen. Before joining Tübingen, he was a postdoc (Marie Curie fellowship) at the Seminar für Statistik, ETH Zurich (CH). During his PhD and Postdoc he has been working with Dominik Janzing and Bernhard Schölkopf at the MPI for Intelligent Systems, Tübingen (GER), and later with Peter Bühlmann and Nicolai Meinshausen at ETH Zurich; his thesis received the ETH medal. Jonas has been working with Leon Bottou at Microsoft Research (WA, USA), Martin Wainwright at UC Berkeley (CA, USA) and Peter Spirtes at CMU (Pittsburgh, USA). He studied Mathematics in Heidelberg (GER) and in Cambridge (UK).

A full CV can be found here.



(Co-)organized Events









IMS, Bernoulli Society, ISI



Selected Talks

Causality in 4 Steps

  1. Consider the following problem: we are given data from gene A (or B) and a phenotype. Clearly, both variables are correlated. What is the best prediction for the phenotype given we are deleting gene A (or B), such that its activity becomes zero?


  2. Causality matters: Intuitively, the optimal prediction should depend on the underlying causal structure:
    But then, if we do not accept any form of causal notion, we cannot distinguish between these two cases and our best prediction must be: "I do not know."!


  3. Causal Model: 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.


  4. Examples of questions that are studied in this field: How can one compute intervention distributions from the graph and the observational distribution efficiently? What if some of the variables are unobserved? What are nice graphical representations? Under which assumptions can we reconstruct the causal model from the observational distribution ("causal discovery")? What if we are also given data from some of the intervention distributions? Does causal knowledge help in more "classical" tasks in machine learning and statistics?



Causality Script

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



Deutsche SchülerAkademie