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

Most of the publications are also on Google Scholar.

 

Bib-Entries

@book{Peters2017,
author = {Peters, J. and Janzing, D. and Sch\"olkopf, B.},
title = {Elements of Causal Inference: Foundations and Learning Algorithms},
address = {Cambridge, MA, USA},
publisher = {MIT Press},
year = {2017}
}

@article{Pfister2017jrssb,
author = {Pfister, N. and B\"uhlmann, P. and Sch\"olkopf, B. and Peters, J.},
title = {Kernel-based tests for joint independence},
journal = {Journal of the Royal Statistical Society: Series B},
volume = {80},
pages = {5--31},
year = {2017}
}

@article{Meinshausen2016pnas,
author = {N. Meinshausen and A. Hauser and J. Mooij and J. Peters and P. Versteeg and P. B\"uhlmann},
title = {Methods for Causal inference from gene perturbation experiments and validation},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {27},
pages = {7361--7368},
year = {2016}
}

@article{Schoelkopf2016pnas,
author = {B. Sch\"olkopf and D. Hogg and D. Wang and D. Foreman-Mackey and D. Janzing and C.-J. Simon-Gabriel and J. Peters},
title = {Modeling Confounding by Half-Sibling Regression},
journal = {Proceedings of the National Academy of Sciences},
volume = {113},
number = {27},
pages = {7391--7398},
year = {2016}
}

@article{Peters2016jrssb,
author = {J. Peters and P. B\"uhlmann and N. Meinshausen},
year = {2016},
title = {Causal inference using invariant prediction: identification and confidence intervals},
volume = {78},
number = {5},
pages = {947--1012},
journal = {Journal of the Royal Statistical Society, Series B (with discussion)}
}
% ArXiv e-prints (1501.01332)

@inproceedings{Bauer2016icml,
author = {S. Bauer and B. Sch\"olkopf and J. Peters},
title = {The Arrow of Time in Multivariate Time Series},
booktitle = {Proceedings of the 33rd International Conference on Machine Learning ({ICML})},
pages = {2043--2051},
publisher = {Journal of Machine Learning Research: Workshop and Conference Proceedings},
year = {2016},
}

@article{Mooij2016jmlr,
author = {J. M. Mooij and J. Peters and D. Janzing and J. Zscheischler and B. Sch\"olkopf},
year = {2016},
volume = {17},
number = {32},
pages = {1--102},
title = {Distinguishing cause from effect using observational data: methods and benchmarks},
journal = {Journal of Machine Learning Research}
}

@article{Sippel2015grl,
title = {Quantifying changes in climate variability and extremes: Pitfalls and their overcoming},
author = {S. Sippel and J. Zscheischler and M. Heimann and F. E. L. Otto and J. Peters and M. D. Mahecha},
journal = {Geophysical Research Letters},
volume = {42},
number = {22},
pages = {9990--9998},
year = {2015}
}

@inproceedings{Rothenhaeusler2015,
title = {{backShift}: Learning causal cyclic graphs from unknown shift interventions},
author = {D. Rothenh{\"a}usler and C. Heinze and J. Peters and N. Meinshausen},
booktitle = {Advances in Neural Information Processing Systems 28 ({NIPS})},
pages = {1513--1521},
publisher = {Curran Associates, Inc.},
year = {2015}
}

@inproceedings{Schoelkopf2015icml,
author = {B. Sch\"{o}lkopf and D. W. Hogg and D. Wang and D. Foreman-Mackey and D. Janzing and C.-J. Simon-Gabriel and J. Peters},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning ({ICML})},
title = {Removing systematic errors for exoplanet search via latent causes},
pages = {2218--2226},
publisher = {ACM Press},
year = {2015}
}
% address = {New York, NY, USA},

@article{Schoelkopf2015standcomp,
title = {Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations},
author = {B. Sch{\"o}lkopf and K. Muandet and K. Fukumizu and S. Harmeling and J. Peters},
journal = {Statistics and Computing },
volume = {25},
number = {4},
pages = {755--766},
year = {2015}
}

@´╗┐article{Peters2015neco,
author = {J. Peters and P. B\"uhlmann},
title = {Structural Intervention Distance ({SID}) for Evaluating Causal Graphs},
journal = {Neural Computation},
volume = {27},
pages = {771--799},
year = {2015}
}
% ArXiv e-prints (1306.1043)

@article{Peters2014jci,
author = {J. Peters},
year = {2014},
title = {On the Intersection Property of Conditional Independence and its Application to Causal Discovery},
journal = {Journal of Causal Inference},
pages = {97--108},
volume = {3}
}
% ArXiv e-prints (1403.0408)

@article{Buehlmann2014annals,
author = {P. {B{\"u}hlmann} and J. Peters and J. Ernest},
title = {{CAM}: Causal Additive Models, high-dimensional order search and penalized regression},
journal = {Annals of Statistics},
volume = {42},
pages = {2526--2556},
year = {2014}
}

@article{Peters2014jmlr,
author = {J. Peters and J. M. Mooij and D. Janzing and B. Sch\"olkopf},
title = {Causal Discovery with Continuous Additive Noise Models},
year = {2014},
journal = {Journal of Machine Learning Research},
volume = {15},
pages = {2009--2053}
}

@inproceedings{Peters2013nips,
author = {J. Peters and D. Janzing and B. Sch\"olkopf},
title = {Causal Inference on Time Series using Structural Equation Models},
booktitle = {{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems 26 ({NIPS})},
pages = {585--592},
publisher = {Curran Associates, Inc.},
year = {2013}
}

@article{Peters2014biometrika,
author = {J. Peters and P. B{\"u}hlmann},
title = {Identifiability of {G}aussian Structural Equation Models with Equal Error Variances},
journal = {Biometrika},
pages = {219--228},
volume = {101},
number = {1},
year = {2014}
}

@article{Bottou2013jmlr,
author = {L. Bottou and J. Peters and J. Qui{\~n}onero-Candela and D. X. Charles and D. M. Chickering and E. Portugualy and D. Ray and P. Simard and E. Snelson},
title = {Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising},
journal = {Journal of Machine Learning Research},
volume = {14},
pages = {3207--3260},
year = {2013}
}

@inproceedings{Sgouritsa2013uai,
author = {E. Sgouritsa and D. Janzing and J. Peters and B. Sch\"{o}lkopf},
title = {Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders},
booktitle = {Proceedings of the 29th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {556--565},
publisher = {AUAI Press},
year = {2013}
}
% address = {Corvallis, Oregon, USA},

@inproceedings{Schoelkopf2012icml,
author = {B. Sch\"{o}lkopf and D. Janzing and J. Peters and E. Sgouritsa and K. Zhang and J. M. Mooij},
booktitle = {Proceedings of the 29th International Conference on Machine Learning ({ICML})},
title = {On causal and anticausal learning},
pages = {1255--1262},
publisher = {Omnipress},
year = {2012}
}
% editor = {John Langford and Joelle Pineau},
% address = {New York, NY, USA},

@inproceedings{Peters2011uai,
author = {J. Peters and J. M. Mooij and D. Janzing and B. Sch\"{o}lkopf},
title = {Identifiability of Causal Graphs using Functional Models},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {589--598},
publisher = {AUAI Press},
year = {2011}
}
% address = {Corvallis, Oregon, USA},

@inproceedings{Janzing2011uai,
author = {D. Janzing and E. Sgouritsa and O. Stegle and J. Peters and B. Sch\"{o}lkopf},
title = {Detecting low-complexity unobserved causes},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {383--391},
publisher = {AUAI Press},
year = {2011}
}
% address = {Corvallis, Oregon, USA},

@inproceedings{Zhang2011uai,
author = {K. Zhang and J. Peters and D. Janzing and B. Sch{\"o}lkopf},
title = {Kernel-based Conditional Independence Test and Application in Causal Discovery},
booktitle = {Proceedings of the 27th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI})},
pages = {804--813},
publisher = {AUAI Press},
year = {2011}
}
%address = {Corvallis, Oregon, USA},

@article{Peters2011tpami,
author = {J. Peters and D. Janzing and B. Sch\"{o}lkopf},
title = {Causal Inference on Discrete Data Using Additive Noise Models},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {33},
year = {2011},
pages = {2436-2450},
publisher = {IEEE Computer Society}
}
%address = {Los Alamitos, CA, USA},
%doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.71},

@inproceedings{Peters2010aistats,
author = {J. Peters and D. Janzing and B. Sch\"{o}lkopf},
title = {Identifying Cause and Effect on Discrete Data using Additive Noise Models},
booktitle = {Proceedings of the 13th conference on Artificial Intelligence and Statistics ({AIStats})},
publisher = {Journal of Machine Learning Research: Workshop and Conference Proceedings},
volume = {9},
year = {2010},
pages = {597--604}
}

@inproceedings{Janzing2009uai,
title = {Identifying confounders using additive noise models},
author = {D. Janzing and J. Peters and J. M. Mooij and B. Sch\"{o}lkopf},
booktitle = {Proceedings of the 25th Annual Conference on {U}ncertainty in {A}rtificial {I}ntelligence ({UAI} )},
publisher = {AUAI Press},
pages = {249--257},
year = {2009}
}
% address = {Corvallis, Oregon, USA},

@inproceedings{Mooij2009icml,
author = {J. M. Mooij and D. Janzing and J. Peters and B. Sch\"{o}lkopf},
booktitle = {Proceedings of the 26th International Conference on Machine Learning ({ICML})},
pages = {745--752},
title = {Regression by Dependence Minimization and its Application to Causal Inference},
publisher = {ACM Press},
year = {2009}
}

@inproceedings{Peters2009icml,
author = {J. Peters and D. Janzing and A. Gretton and B. Sch\"olkopf},
title = {Detecting the Direction of Causal Time Series},
booktitle = {Proceedings of the 26th International Conference on Machine Learning ({ICML})},
pages = {801--808},
publisher = {ACM Press},
year = {2009}
}

@inproceedings{Hoyer2009nips,
author = {P. O. Hoyer and D. Janzing and J. M. Mooij and J. Peters and B. Sch\"olkopf},
title = {Nonlinear causal discovery with additive noise models},
booktitle = {{A}dvances in {N}eural {I}nformation {P}rocessing {S}ystems 21 ({NIPS})},
pages = {689--696},
publisher = {Curran Associates, Inc.},
year = {2009}
}

@inproceedings{Peters2009gfkl,
author = {J. Peters and D. Janzing and A. Gretton and B. Sch{\"o}lkopf},
title = {Kernel methods for detecting the direction of time series},
booktitle = {Proccedings of the 32nd Annual Conference of the German Classification Society (GfKl 2008)},
year = {2009},
pages = {1--10}
}

@PhDThesis{Peters2012phd,
author = {J. Peters},
title = {Restricted Structural Equation Models for Causal Inference},
School = {ETH Zurich and MPI for Intelligent Systems},
year = {2012},
note = {\url{http://dx.doi.org/10.3929/ethz-a-007597940}}
}

@misc{Peters2008diploma,
author = {J. Peters},
title = {Asymmetries of Time Series under Inverting their Direction},
howpublished = {Diploma Thesis, University of Heidelberg},
year = {2008},
note = {\url{http://stat.ethz.ch/people/jopeters}}
}