Research Group Leader

Office: 206

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+49 7071 601 540

+49 7071 601 552

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

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!

Before joining Tuebingen, I was a postdoc (Marie Curie fellowship) at the Seminar für Statistik, ETH Zurich (CH). During my PhD and Postdoc I have 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. 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).

- March 6th-12th 2016, I will participate in the Oberwolfach workshop "Comp. and Stat. Efficient Inference for Complex Large-scale Data"
- November 26th-27th 2015, I will talk at the workshop "Exploring the earth system data cube" in Jena.
- October 10th 2015, I will give a tutorial on causality at GCPR 2015.
- September 25th 2015, I will talk at the DMV mini-symposium "Statistics on complex structures"
- Since June 1st 2015, I am Fellow of the Max Planck ETH Center for Learning Systems.

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.

- Nov 2015: Workshop on "Exploring the earth system data cube", Jena
- 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

- 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

- Jul 2015, Workshop on Advances in Causal Inference at UAI 2015, Amsterdam, The Netherlands (co-organizer)
- Apr 2015, Workshop on Networks: Processes and Causality at DALI 2015, La Palma.
- Jul 2014, Workshop on Causal Inference: Learning and Prediction at UAI 2014, Quebec, Canada (co-organzier)

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

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

- Lecture: Intelligente Systeme I - Empirische Inferenz (with M. Hirsch), University of Tuebingen, summer semester 2015
- Lecture: Causality (with M. Maathuis and N. Meinshausen), 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

- Kausalitaet, Exoplaneten und Black Jack: wie man aus Daten lernt, Sommerakademie 2015
- Akademieleitung, Deutsche SchülerAkademie 2013, 2014
- Wahrscheinlichkeiten als Sprache, Deutsche SchülerAkademie 2012
- Woher kommt der Strom?, Deutsche SchülerAkademie 2011
- It's Magic, Deutsche SchülerAkademie 2010
- Huete, Naegel, Schuhkartons, Deutsche SchülerAkademie 2009

- 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

- Course on Causality (with B. Schoelkopf), Machine Learning Summer School Tuebingen, summer 2015

- R-code for
**Half-Sibling Regression**(only simulations on iid data). - R-code for
**SID**can 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**. 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**. 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). - R-code for
**Invariant Prediction**can be downloaded from CRAN, package name: "InvariantCausalPrediction". Paper: J. Peters, P. Bühlmann, N. Meinshausen: Causal inference using invariant prediction: identification and confidence intervals, arXiv:1501.01332.

- M. Rojas-Carulla, B. Schölkopf, R. Turner,
**J. Peters**: A Causal Perspective on Domain Adaptation, arXiv:1507.05333 **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

- D. Rothenhäusler, C. Heinze,
**J. Peters**, N. Meinshausen: backShift: Learning causal cyclic graphs from unknown shift interventions, Advances in Neural Information Processing Systems 26 (NIPS 2015, accepted), arXiv:1506.02494, 2015. - B. Schölkopf, D. Wang, D. Hogg, D. Foreman-Mackey, D. Janzing, C-J. Simon-Gabriel,
**J. Peters**: Removing systematic errors for exoplanet search via latent causes, 32nd International Conference on Machine Learning (ICML 2015), 2218-2226, 2015. - B. Schölkopf, K. Muandet, K. Fukumizu, S. Harmeling,
**J. Peters**: Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations, Statistics and Computing 25:755-766, 2015. **J. Peters**, P. Bühlmann: Structural Intervention Distance (SID) for Evaluating Causal Graphs, Neural Computation 27:771-799, 2015. bibtex**J. Peters**: On the Intersection Property of Conditional Independence and its Application to Causal Discovery, Journal of Causal Inference 3:97-108, 2015. bibtex- 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 **J. Peters**, J. Mooij, D. Janzing, B. Schölkopf: Causal Discovery with Continuous Additive Noise Models, JMLR 15:2009-2053, 2014. bibtex**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**J. Peters**, P. Bühlmann: Identifiability of Gaussian Structural Equation Models with Equal Error Variances, Biometrika, 101:219-228, 2014. bibtex- 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 - 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 - 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 **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- 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 - 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 **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**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- 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 - 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 **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- 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

- 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

- 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

A current version of my CV (Oct 2015) can be downloaded here.

26 results
(BibTeX)

**Removing systematic errors for exoplanet search via latent causes** In: *Proceedings of The 32nd International Conference on Machine Learning*, 37, 2218–2226, JMLR, ICML 2015
(Conference Paper)

**Modeling Confounding by Half-Sibling Regression**(Article)

**On the Intersection Property of Conditional Independence and its Application to Causal Discovery** *Journal of Causal Inference*, 3(1):97-108
(Article)

**Structural Intervention Distance (SID) for Evaluating Causal Graphs** *Neural Computation *, 27(3):771-799
(Article)

**Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations** *Statistics and Computing *, 25(4):755–766
(Article)

**CAM: Causal Additive Models, high-dimensional order search and penalized regression** *Annals of Statistics*, 42(6):2526-2556
(Article)

**Identifiability of Gaussian Structural Equation Models with Equal Error Variances** *Biometrika*, 101(1):219–228
(Article)

**Causal Discovery with Continuous Additive Noise Models ** *Journal of Machine Learning Research*, 15, 2009-2053
(Article)

**Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising** *Journal of Machine Learning Research*, 14, 3207–3260
(Article)

**Semi-supervised learning in causal and anticausal settings** In: *Empirical Inference*, 129–141, Springer-Verlag
(Book Chapter)

**Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders ** In: *Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI)*, 556-565, AUAI Press Corvallis, Oregon, USA, UAI 2013
(Conference Paper)

**Causal Inference on Time Series using Restricted Structural Equation Models** In: *Advances in Neural Information Processing Systems 26*, 154-162, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
(Conference Paper)

Peters, J. (2012). **Restricted structural equation models for causal inference** ETH Zurich
(Thesis)

**On Causal and Anticausal Learning** In: *Proceedings of the 29th International Conference on Machine Learning (ICML)*, 1255-1262, Omnipress, New York, NY, USA, ICML 2012
(Conference Paper)

**Detecting low-complexity unobserved causes** 383-391, AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
(Conference Paper)

**Identifiability of causal graphs using functional models** 589-598, AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
(Conference Paper)

**Causal Inference on Discrete Data using Additive Noise Models** *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 33(12):2436-2450
(Article)

**Kernel-based Conditional Independence Test and Application in Causal Discovery** 804-813, AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)
(Conference Paper)

**Identifying Cause and Effect on Discrete Data using Additive Noise Models** In: *JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010*, *Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)*, 597-604, JMLR, Cambridge, MA, USA, 13th International Conference on Artificial Intelligence and Statistics
(Conference Paper)

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

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!

Before joining Tuebingen, I was a postdoc (Marie Curie fellowship) at the Seminar für Statistik, ETH Zurich (CH). During my PhD and Postdoc I have 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. 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).

- March 6th-12th 2016, I will participate in the Oberwolfach workshop "Comp. and Stat. Efficient Inference for Complex Large-scale Data"
- November 26th-27th 2015, I will talk at the workshop "Exploring the earth system data cube" in Jena.
- October 10th 2015, I will give a tutorial on causality at GCPR 2015.
- September 25th 2015, I will talk at the DMV mini-symposium "Statistics on complex structures"
- Since June 1st 2015, I am Fellow of the Max Planck ETH Center for Learning Systems.

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.

- Nov 2015: Workshop on "Exploring the earth system data cube", Jena
- 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

- 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

- Jul 2015, Workshop on Advances in Causal Inference at UAI 2015, Amsterdam, The Netherlands (co-organizer)
- Apr 2015, Workshop on Networks: Processes and Causality at DALI 2015, La Palma.
- Jul 2014, Workshop on Causal Inference: Learning and Prediction at UAI 2014, Quebec, Canada (co-organzier)

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

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

Office: 206

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+49 7071 601 540

+49 7071 601 552