Ei_header
Thumb_dsa
Jonas Peters
Position: Group Leader
Room no.: 206
Phone: +49 7071 601 540
Fax: +49 7071 601 552

Overview

Currently, I am a group leader for the causality group at the MPI Tuebingen.

My work focusses 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!

Before joining Tuebingen, I was a postdoc (Marie Curie fellowship) at the Seminar für Statistik, ETH Zurich (CH). During my PhD I have been working with Dominik Janzing and Bernhard Schölkopf at the MPI for Intelligent Systems, Tübingen (GER), and later with Peter Bühlmann at the ETH Zurich. I have spent three months with Leon Bottou at Microsoft Research (WA, USA) in 2011 and two months with Martin Wainwright at UC Berkeley (CA, USA) in 2013. In 2014, I have been working with Peter Spirtes at CMU (Pittsburgh, USA) for two months. I studied Mathematics in Heidelberg (GER) and in Cambridge (UK).

 

News

 

Invited Talks: Conferences and Workshops

  • Oct 2015: Tutorial at GCPR, Aachen
  • Sep 2015: DMV - Minisymposium Statistics on Complex Structures, Hamburg
  • Jul 2015: ISI - World Statistics Congress, Rio de Janeiro
  • Mar 2015: Workshop on Big Data in Health Policy, Toronto
  • Mar 2015: Causation from Correlation?, Die Junge Akademie, Ohlstadt
  • Dec 2014: ERCIM, Working Group CMStatistics, Pisa
  • Jul 2014: IMS Annual Meeting, Sydney
  • Jun 2014: Workshop on Simplicity and Causal Discovery, Pittsburgh
  • Dec 2013: NIPS Workshop on Causality, Lake Tahoe
  • Sep 2012: Networks: Processes and Causality, Menorca
  • Sep 2010: International Symposium on Quantum Thermodynamics, Stuttgart
  • Oct 2009: Machine Learning approaches to statistical dep. and causality, Schloss Dagstuhl

 

Invited Talks: Research Visits

  • Jan 2015: University of St. Andrews, St Andrews
  • Jan 2015: Statslab, University of Cambridge, Cambridge
  • Jan 2015: Microsoft Research, Cambridge
  • Jan 2015: UCL Seminar Series, London
  • Jan 2015: WIAS, Berlin
  • Jul 2014: UCLA, Los Angeles
  • Jul 2014: Caltech, Pasadena
  • Mar 2014: University of Regensburg, Regensburg
  • Jan 2014: University of Amsterdam, Amsterdam
  • Jan 2014: University of Nijmegen, Nijmegen
  • Nov 2013: UC Berkeley, Berkeley
  • Dec 2012: IST Austria, Vienna
  • Nov 2012: MPI for Dynamics and Self-Organization, Goettingen
  • Jun 2012: Teleconference Causality
  • Jun 2011: Seminar for Statistics, ETH Zurich, Zurich
  • Jun 2010: MPI for Biogeochemistry, Jena
  • Mar 2009: Teleconference Causality

 

Reviewing

Journals

  • ACM Transactions on Intelligent Systems and Technology,
  • Annals of Statistics
  • Bernoulli Journal
  • IEEE Transactions of Pattern Analysis and Machine Intelligence, 
  • IEEE Information Theory,
  • Journal of Machine Learning Research,
  • Neurocomputing,
  • NeuroImage,
  • Statistics and Computing

Conferences

  • ICML (2012, 2013, 2014)
  • ICONIP 2011,
  • IEEE Int. Workshop on ML for Signal Proc. (2012)
  • NIPS (2011)
  • COLT 2015
  • UAI (2012, 2013, 2014, 2015)

Lecturing

  • Lecture: Intelligente Systeme I - Empirische Inferenz (with M. Hirsch), University of Tuebingen, summer semester 2015
  • Lecture: Causality, ETH Zurich, spring semester 2015
  • Seminar: Functional Data Analysis (with H. R. Kuensch), ETH Zurich, spring semester 2014
  • Seminar: Causal Inference from Observational Data, ETH Zurich, spring semester 2013

 

Assisting

  • Foundations of Mathematical Statistics, ETH Zurich, autumn semester 2012
  • Computational Statistics, ETH Zurich, spring semester 2012
  • Introduction to Statistics, University of Heidelberg, summer semester 2006
  • Analysis II, University of Heidelberg, winter semester 2005
  • Analysis I, University of Heidelberg, summer semester 2005

 

Deutsche SchuelerAkademie / Sommerakademie

  • R-code for SID can soon be downloaded from CRAN, package name: "SID". Paper: J. Peters, P. Bühlmann: "Structural Intervention Distance (SID) for Evaluating Causal Graphs", Neural Computation, 2015.
  •  R-code for CAM can be downloaded from CRAN, package name: "CAM". Paper: P. Bühlmann, J. Peters, J. Ernest: CAM: Causal Additive Models, high-dimensional Order Search and Penalized Regression, Annals of Statistics, 2014.
  • R-code for Timino can be downloaded here. Paper: J. Peters, D. Janzing, B. Schölkopf: Causal Inference on Time Series using Structural Equation Models, Advances in Neural Information Processing Systems 26, 2014.
  • R-code for ANMs can be downloaded here. Paper: J. Peters, J. Mooij, D. Janzing, B. Schölkopf: Causal Discovery with Continuous Additive Noise Models, JMLR, 2014.
  • Matlab-code for Cause-Effect-Pairs (same paper) can be downloaded here
  • R-code for ANMs with equal error variances can be downloaded here. Paper: J. Peters, P. Bühlmann: Identifiability of Gaussian Structural Equation Models with Equal Error Variances, Biometrika, 2014

Preprints

  • B. Schölkopf, K. Muandet, K. Fukumizu, J. Peters: Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations (submitted)
  • J. Peters, P. Bühlmann, N. Meinshausen: Causal inference using invariant prediction: identification and confidence intervals, arXiv:1501.01332
  • J. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schölkopf: Distinguishing cause from effect using observational data: methods and benchmarks, arXiv:1412.3773

 

Peer-reviewed

  1. J. Peters, P. Bühlmann: Structural Intervention Distance (SID) for Evaluating Causal Graphs, Neural Computation 27:771-799, 2015. bibtex
  2. J. Peters: On the Intersection Property of Conditional Independence and its Application to Causal Discovery, Journal of Causal Inference 3:97-108, 2015. bibtex
  3. P. Bühlmann, J. Peters, J. Ernest: CAM: Causal Additive Models, high-dimensional Order Search and Penalized Regression, Annals of Statistics 42:2526-2556, 2014. bibtex
  4. J. Peters, J. Mooij, D. Janzing, B. Schölkopf: Causal Discovery with Continuous Additive Noise Models, JMLR 15:2009-2053, 2014. bibtex
  5. J. Peters, D. Janzing, B. Schölkopf: Causal Inference on Time Series using Structural Equation Models, Advances in Neural Information Processing Systems 26 (NIPS 2013), 154-162, 2014. bibtex
  6. J. Peters, P. Bühlmann: Identifiability of Gaussian Structural Equation Models with Equal Error Variances, Biometrika, 101:219-228, 2014. bibtex
  7. L. Bottou, J. Peters, J. Quiñonero-Candela, D. X. Charles, D. M. Chickering, E. Portugaly, D. Ray, P. Simard, E. Snelson: Counterfactual Reasoning and Learning Systems, JMLR 14:3207-3260, 2013. bibtex
  8. E. Sgouritsa, D. Janzing, J. Peters, B. Schölkopf: Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders, 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013), 556-565, 2013. bibtex
  9. B. Schölkopf, D. Janzing, J. Peters, E.Sgouritsa, K.Zhang, J. M. Mooij: On causal and anticausal learning, 29th International Conference on Machine Learning (ICML 2012), 1255-1262, 2012. bibtex
  10. J. Peters, J. M. Mooij, D. Janzing, B. Schölkopf: Identifiability of Causal Graphs using Functional Models, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, USA, 589-598, 2011. bibtex
  11. D. Janzing, E. Sgouritsa, O. Stegle, J. Peters, B. Schölkopf: Detecting low-complexity unobserved causes, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, USA, 383-391, 2011. bibtex
  12. K. Zhang, J. Peters, D. Janzing, B. Schölkopf: Kernel-based Cond. Independence Test and Application in Causal Discovery, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, USA, 804-813. bibtex
  13. J. Peters, D. Janzing, B. Schölkopf: Causal Inference on Discrete Data using Additive Noise Models, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33:2436-2450, 2011. bibtex
  14. J. Peters, D. Janzing, B. Schölkopf: Identifying Cause and Effect on Discrete Data using Additive Noise Models, JMLR Workshop and Conference Proceedings Volume 9: 13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), MIT Press, Cambridge, MA, USA, 597-604, 2010. (conference version of TPAMI 2011) bibtex
  15. D. Janzing, J. Peters, J. M. Mooij, B. Schölkopf: Identifying Confounders Using Additive Noise Models, 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, USA, 249-257, 2009. bibtex
  16. J. M. Mooij, D. Janzing, J. Peters, B. Schölkopf: Regression by Dependence Minimization and its Application to Causal Inference in Additive Noise Models, 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 745-752, 2009. bibtex
  17. J. Peters, D. Janzing, A. Gretton, B. Schölkopf: Detecting the Direction of Causal Time Series, 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 801-808, 2009. bibtex
  18. P. Hoyer, D. Janzing, J. M. Mooij, J. Peters, B. Schölkopf: Nonlinear Causal Discovery with Additive Noise Models, Advances in Neural Information Processing Systems 21 (NIPS 2008), Curran, Red Hook, NY, USA, 689-696, 2009. bibtex

 

Theses

  • PhD Thesis: Restricted Structural Equation Models for Causal Inference, ETH Zurich, 2012. This version includes minor corrections which can be downloaded separately: Errata. bibtex
  • Diploma Thesis: Asymmetries of Time Series under Inverting their Direction, University of Heidelberg, 2008. bibtex

 

Other

  • B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, J. Mooij: Semi-supervised learning in causal and anticausal settings In: Empirical Inference, (Ed) B. Schölkopf, Z. Luo, and V. Vovk, Springer-Verlag, 129-141, 2013. 
  • J. Peters, D. Janzing, A. Gretton, B. Schölkopf: Kernel Methods for Detecting the Direction of Time Series, GfKl 2008, 32nd Annual Conference of the German Classification Society, Springer, Berlin, Germany, 57-66, 2008. bibtex

Export search results as: [BibTex]

2015
Articles
2014
Articles
  • P Bühlmann, J. Peters, J. Ernest (2014). CAM: Causal Additive Models, high-dimensional order search and penalized regression Annals of Statistics, 42, (6), 2526-2556
  • J. Peters, P. Bühlman (2014). Identifiability of Gaussian Structural Equation Models with Equal Error Variances Biometrika, 101, (1), 219–228
2013
Articles
  • L. Bottou, J. Peters, J. Quiñonero-Candela, D.X. Charles, D.M. Chickering, E. Portugualy, D. Ray, P. Simard, E. Snelson (2013). Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising Journal of Machine Learning Research, 14, 3207–3260
Conference Papers
  • E. Sgouritsa, D. Janzing, J. Peters, B. Schölkopf (2013). Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), (Ed) A Nicholson and P Smyth, AUAI Press Corvallis, Oregon, USA, 556-565, UAI 2013
  • J. Peters, D. Janzing, B. Schölkopf (2013). Causal Inference on Time Series using Restricted Structural Equation Models In: Advances in Neural Information Processing Systems 26, (Ed) C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, 154-162, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
Contributions to books
2012
Conference Papers
2011
Conference Papers
  • K. Zhang, J. Peters, D. Janzing, B. Schölkopf (2011). Kernel-based Conditional Independence Test and Application in Causal Discovery (Ed) FG Cozman and A Pfeffer, AUAI Press, Corvallis, OR, USA, 804-813, ISBN: 978-0-9749039-7-2, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
Articles
2010
Conference Papers
  • J. Peters, D. Janzing, A. Gretton, B. Schölkopf (2010). Kernel Methods for Detecting the Direction of Time Series In: Advances in Data Analysis, Data Handling and Business Intelligence, (Ed) A Fink and B Lausen and W Seidel and A Ultsch, Advances in Data Analysis, Data Handling and Business Intelligence, Springer, Gesellschaft für Klassifikation, Berlin, Germany, 57-66, ISBN: 978-3-642-01044-6, 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008)
  • J. Peters, D. Janzing, B. Schölkopf (2010). Identifying Cause and Effect on Discrete Data using Additive Noise Models In: JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, (Ed) YW Teh and M Titterington, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), JMLR, Cambridge, MA, USA, 597-604, 13th International Conference on Artificial Intelligence and Statistics
2009
Conference Papers
  • PO. Hoyer, D. Janzing, JM. Mooij, J. Peters, B. Schölkopf (2009). Nonlinear causal discovery with additive noise models In: Advances in neural information processing systems 21, (Ed) D Koller and D Schuurmans and Y Bengio and L Bottou, Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008, Curran, Red Hook, NY, USA, 689-696, ISBN: 978-1-605-60949-2, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008)
  • J. Peters, D. Janzing, A. Gretton, B. Schölkopf (2009). Detecting the Direction of Causal Time Series In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk and L Bottou and ML Littman, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 801-808, ICML 2009
  • JM. Mooij, D. Janzing, J. Peters, B. Schölkopf (2009). Regression by dependence minimization and its application to causal inference in additive noise models In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk and L Bottou and M Littman, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 745-752, ICML 2009
  • D. Janzing, J. Peters, JM. Mooij, B. Schölkopf (2009). Identifying confounders using additive noise models In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, (Ed) J Bilmes and AY Ng, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, Corvallis, OR, USA, 249-257, ISBN: 978-0-9749039-5-8 , UAI 2009
2008
Theses
  • J. Peters (2008). Asymmetries of Time Series under Inverting their Direction University of Heidelberg