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





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.



Open Positions

Open positions are announced on All details can be discussed during the application process. My apologies that I cannot answer to all individual emails before the deadline.



Research Group



Book on Mathematical Games

We have written a book on mathematical games that will appear at MIT Press.

Jonas Peters, Nicolai Meinshausen: The Raven's Hat: Fallen Pictures, Rising Sequences, and Other Mathematical Games

Link to MIT Press



Book on Causality

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

Link to bibtex

Link to MIT Press


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).



Causality Script

I have written a script on causality. Almost all of it made it into our book. It is a bit shorter but less polished. It can be downloaded here.



Popular Science



Scholarships and Awards

Guy Medal in Bronze, awarded by the Royal Statistical Society (2019), ASA Causality in Statistics Education Award (2018; with D. Janzing and B. Schölkopf), Teacher of the year at the faculty of SCIENCE, University of Copenhagen (2018), Member of the Junge Akademie (since 2016; board member since 2017), Marie Curie fellowship (2013--2015), ETH medal for an outstanding PhD thesis (2013), scholarhsip of the Studienstiftung des deutschen Volkes (2004--2008), UNWIN prize and election to scholar (Downing College, Cambridge) (2007), European Excellence Programme (DAAD), Kurt-Hahn-Trust, Hölderlin Programme (Allianz) (2006--2007), Deutsche SchülerAkademie (2001)




Jonas is professor of statistics at the Department of Mathematical Sciences at the University of Copenhagen. Previously, he has associate professor at the same department, a group leader at the Max-Planck-Institute for Intelligent Systems in Tuebingen and a Marie Curie fellow (postdoc) at the Seminar for Statistics, ETH Zurich. He studied mathematics at the University of Heidelberg and the University of Cambridge and did his PhD both at the MPI Tuebingen and ETH Zurich. He tries to infer causal relationships from different types of data and is interested in building statistical methods that are robust with respect to distributional shifts. In his research, Jonas seeks to combine theory, methodology, and applications. His work relates to areas such as computational statistics, causal inference, graphical models, independence testing or high-dimensional statistics.

If you are interested in a full CV, please send me an email.







Bernoulli Society, Danish Society for Theoretical Statistics, IMS, ISI (elected), Royal Statistical Society



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?




Kammermusikkreis Unterwachingen


Concerts in Copenhagen

Deutsche SchülerAkademie