I try to make sure that all my papers are available as pdf. Please tell me if you find that one of the links is broken.
Most of the publications are also on Google Scholar.
Preprints
- S. Tiedemann, J. Sanchez Canales, F. Schur, R. Sgarlato, L. Hirth, O. Ruhnau, J. Peters:
Identifying Elasticities in Autocorrelated Time Series Using Causal Graphs,
arXiv 2409.15530
- F. Schur, J. Peters:
DecoR: Deconfounding Time Series with Robust Regression,
arXiv 2406.07005
- M. Lazzaretto, J. Peters, N. Pfister:
Invariant Subspace Decomposition,
arXiv 2404.09962
- S. Huang, J. Peters, and N. Pfister:
Causal Change Point Detection and Localization,
arXiv 2403.12677
- N. Gnecco, J. Peters, S. Engelke, and N. Pfister:
Boosted Control Functions,
arXiv 2310.05805
- F. Jørgensen, S. Weichwald, J. Peters:
Unfair Utilities and First Steps Towards Improving Them,
arXiv 2306.00636
Peer-reviewed
- J. Gamella, J. Peters, P. Buhlmann:
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology,
Nature Machine Intelligence (accepted), 2024+. arXiv
- N. Thams, R. Søndergaard, S. Weichwald, J. Peters:
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past,
Journal of Machine Learning Research 25(302), 1--51, 2024. arXiv
- L. Kook, S. Saengkyongam, A. R. Lundborg, T. Hothorn, J. Peters:
Model-based causal feature selection for general response types,
Journal of the American Statistical Association (accepted), 2024. arXiv
- S. Saengkyongam, E. Rosenfeld, P. Ravikumar, N. Pfister, J. Peters:
Identifying Representations for Intervention Extrapolation,
International Conference on Learning Representations, 2024. arXiv
- S. Saengkyongam, N. Pfister, P. Klasnja, S. Murphy, J. Peters:
Effect-Invariant Mechanisms for Policy Generalization,
Journal of Machine Learning Research 25(34), 1--36, 2024. arXiv
- N. Thams, S. Saengkyongam, N. Pfister, J. Peters:
Statistical Testing under Distributional Shifts,
Journal of the Royal Statistical Society, Series B 85(3), 597--663, 2023. arXiv
- S. Saengkyongam, N. Thams, J. Peters, N. Pfister:
Invariant Policy Learning: A Causal Perspective,
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 54(7), 2023. arXiv
- A. R. Lundborg, R. D. Shah, J. Peters:
Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis,
Journal of the Royal Statistical Society, Series B 84(5), 1821--1850, 2022. arXiv
- M. Jakobsen, R. Shah, P. Bühlmann, J. Peters:
Structure Learning for Directed Trees,
Journal of Machine Learning Research 23(159), 1--97, 2022. arXiv
- N. Pfister*, J. Peters*:
Identifiability of Sparse Causal Effects using Instrumental Variables,
38th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 180:1613--1622, 2022.
arXiv
- P. B. Mogensen, N. Thams, J. Peters:
Invariant Ancestry Search,
39th International Conference on Machine Learning (ICML), PMLR 162:15832--15857, 2022.
arXiv
- S. Saengkyongam, L. Henckel, N. Pfister, J. Peters:
Exploiting Independent Instruments: Identification and Distribution Generalization,
39th International Conference on Machine Learning (ICML), PMLR 162:18935-18958, 2022.
arXiv
- M. Jakobsen, J. Peters:
Distributional Robustness of K-class Estimators and the PULSE,
The Econometrics Journal 25(2), 404--432, 2022. arXiv
- R. Christiansen, M. Baumann, T. Kümmerle, M. Mahecha, J. Peters:
Towards Causal Inference for Spatio-Temporal Data: Conflict and Forest Loss in Colombia,
Journal of the American Statistical Association 117(538), 591--601, 2022. arXiv
- S. Weichwald, S. W. Mogensen, T. E. Lee, D. Baumann, O. Kroemer, I. Guyon, S. Trimpe, J. Peters, N. Pfister:
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning,
Proceedings of the NeurIPS 2021 Competition and Demonstration Track, PMLR 176:246–258, 2022.
arXiv
- R. Christiansen, N. Pfister, M. Jakobsen, N. Gnecco, J. Peters:
A causal framework for distribution generalization,
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 44(10), 6614--6630, 2022. arXiv
- M. Migliavacca, T. Musavi, M. D. Mahecha, J. A. Nelson, J. Knauer, D. D. Baldocchi, O. Perez-Priego, R. Christiansen, J. Peters,
K. Anderson, M. Bahn, T. A. Black, P. D. Blanken, D. Bonal, N. Buchmann, S. Caldararu,
A. Carrara, N. Carvalhais, A. Cescatti, J. Chen, J. Cleverly, E. Cremonese, A. R. Desai, T. S. El-Madany, M. M. Farella,
M. Fernández-Martínez, G. Filippa, M. Forkel, M. Galvagno, U. Gomarasca, C. M. Gough, M. Göckede, A. Ibrom, H. Ikawa, I. A. Janssens,
M. Jung, J. Kattge, T. F. Keenan, A. Knohl, H. Kobayashi, G. Kraemer, B. E. Law, M. J. Liddell, X. Ma, I. Mammarella,
D. Martini, C. Macfarlane, G. Matteucci, L. Montagnani, D. E. Pabon-Moreno, C. Panigada, D. Papale, E. Pendall, J. Penuelas, R. P. Phillips, P. B. Reich, M. Rossini,
E. Rotenberg, R. L. Scott, C. Stahl, U. Weber, G. Wohlfahrt, S. Wolf, I. J. Wright, D. Yakir, S. Zaehle & M. Reichstein:
The three major axes of terrestrial ecosystem function,
Nature 598, 468--472, 2021. pdf
- N. Pfister, E. G. William, J. Peters, R. Aebersold, P. Buhlmann:
Stabilizing Variable Selection and Regression,
Annals of Applied Statistics 15(3), 1220--1246, 2021. arXiv
- M. Oberst, N. Thams, J. Peters, D. Sontag:
Regularizing towards Causal Invariance: Linear Models with Proxies,
38th International Conference on Machine Learning (ICML), 8260--8270, 2021. arXiv
- S. Bongers, P. Forre, J. Peters, J. M. Mooij:
Foundations of Structural Causal Models with Cycles and Latent Variables,
Annals of Statistics 49(5), 2885--2915, 2021. arXiv
- N. Gnecco, N. Meinshausen, J. Peters, S. Engelke:
Causal discovery in heavy-tailed models,
Annals of Statistics 49(3), 1755--1778, 2021 arXiv
- D. Rothenhaeusler, P. Bühlmann, N. Meinshausen, J. Peters:
Anchor regression: heterogeneous data meets causality,
Journal of Royal Statistical Society, Series B 83(2), 215--246, 2021.
arXiv
- S. Weichwald, J. Peters:
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness,
Journal of Cognitive Neuroscience 33(2), 226--247, 2021.
arXiv
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M. D. Mahecha, F. Gans, G. Brandt, R. Christiansen, S. E. Cornell, N. Fomferra, G. Kraemer,
J. Peters, P. Bodesheim, G. Camps-Valls, J. F. Donges, W. Dorigo, L. M. Estupinan-Suarez, V. H. Gutierrez-Velez, M. Gutwin, M. Jung, M. C. Londono, D. G. Miralles, P. Papastefanou, M. Reichstein:
Earth system data cubes unravel global multivariate dynamics,
Earth System Dynamics 11(1), 201--234, 2020.
pdf,
- R. D. Shah, J. Peters:
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure,
Annals of Statistics 48(3), 1514--1538, 2020.
pdf,
arXiv
- R. Christiansen, J. Peters:
Switching Regression Models and Causal Inference in the Presence of Latent Variables,
Journal of Machine Learning Research 21(41), 2020,
pdf,
arXiv
- N. Pfister, S. Bauer, J. Peters:
Learning stable and predictive structures in kinetic systems,
Proceedings of the National Academy of Sciences 116(51), 25405--25411, 2019,
pdf,
arXiv
- J. Runge, S. Bathiany, E. Bollt, G. Camps-Valls,
D. Coumou, E. Deyle, C. Glymour, M. Kretschmer,
M. Mahecha, J. Munoz-Mari, E. Van Nes,
J. Peters, R. Quax, M. Reichstein, M. Scheffer,
B. Schoelkopf, P. Spirtes, G. Sugihara, J. Sun,
K. Zhang, J. Zscheischler:
Inferring causation from time series in Earth system sciences,
Nature Communications 10(2553), 2019.
pdf,
- C. Heinze-Deml, J. Peters, N. Meinshausen:
Invariant Causal Prediction for Nonlinear Models,
Journal of Causal Inference 6(2), 2018.
pdf,
arXiv
- M. Rojas-Carulla, B. Schölkopf, R. Turner, J. Peters:
Invariant Models for Causal Transfer Learning,
Journal of Machine Learning Research 19(36):1-34, 2018.
pdf
- N. Pfister, P. Bühlmann, J. Peters:
Invariant Causal Prediction for Sequential Data,
Journal of the American Statistical Association 114(527), 2018.
pdf,
arXiv
- N. Pfister, P. Bühlmann, B. Schölkopf, J. Peters:
Kernel-based Tests for Joint Independence,
Journal of Royal Statistical Society, Series B 80:5-31, 2017. arXiv,
pdf
- N. Meinshausen, A. Hauser, J. Mooij, P. Versteeg, J. Peters, P. Bühlmann:
Causal inference from gene perturbation experiments: methods, software and validation,
Proceedings of the National Academy of Sciences 113(27):7361-7368, 2016.
pdf,
bibtex
- B. Schölkopf, D. Hogg, D. Wang, D. Foreman-Mackey, D. Janzing, C.-J. Simon-Gabriel, J. Peters:
Modeling Confounding by Half-Sibling Regression,
Proceedings of the National Academy of Sciences 113(27):7391-7398, 2016.
pdf,
bibtex
- J. Peters, P. Bühlmann, N. Meinshausen:
Causal inference using invariant prediction: identification and confidence intervals, arXiv:1501.01332,
Journal of the Royal Statistical Society, Series B (with discussion) 78(5):947-1012, 2016.
pdf,
bibtex
- S. Bauer, B. Schölkopf, J. Peters:
The Arrow of Time in Multivariate Time Series, arXiv:1603.00784,
33rd International Conference on Machine Learning (ICML 2016), 2043-2051, 2016.
pdf,
bibtex
- J. Mooij, J. Peters, D. Janzing, J. Zscheischler, B. Schölkopf:
Distinguishing cause from effect using observational data: methods and benchmarks, arXiv:1412.3773,
Journal of Machine Learning Research 17:1-102, 2016.
pdf,
bibtex
- S. Sippel, J. Zscheischler, M. Heimann, F. Otto, J. Peters, M. Mahecha:
Quantifying changes in climate variability and extremes: pitfalls and their overcoming,
Geophysical Research Letters 42:9990-9998, 2015.
pdf,
bibtex
- D. Rothenhäusler*, C. Heinze-Deml*, J. Peters, N. Meinshausen:
backShift: Learning causal cyclic graphs from unknown shift interventions,
Advances in Neural Information Processing Systems 28 (NIPS 2015), 1513-1521, 2015.
pdf,
bibtex
- B. Schölkopf, D. Hogg, D. Wang, 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.
pdf,
bibtex
- 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.
pdf,
bibtex
- J. Peters, P. Bühlmann:
Structural Intervention Distance (SID) for Evaluating Causal Graphs,
Neural Computation 27:771-799, 2015.
journal version,
arXiv,
bibtex
- J. Peters:
On the Intersection Property of Conditional Independence and its Application to Causal Discovery,
Journal of Causal Inference 3:97-108, 2015.
pdf,
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.
pdf,
bibtex
- J. Peters, J. Mooij, D. Janzing, B. Schölkopf:
Causal Discovery with Continuous Additive Noise Models,
Journal of Machine Learning Research 15:2009-2053, 2014.
pdf,
bibtex
- J. Peters, P. Bühlmann:
Identifiability of Gaussian Structural Equation Models with Equal Error Variances,
Biometrika 101(1):219-228, 2014.
arXiv,
bibtex
- J. Peters, D. Janzing, B. Schölkopf:
Causal Inference on Time Series using Restricted Structural Equation Models,
Advances in Neural Information Processing Systems 26 (NIPS 2013), 154-162, 2014.
pdf,
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,
Journal of Machine Learning Research 14:3207-3260, 2013.
pdf,
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.
arXiv,
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.
pdf,
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.
arXiv,
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.
arXiv,
bibtex
- K. Zhang, J. Peters, D. Janzing, B. Schölkopf:
Kernel-based Conditional Independence Test and Application in Causal Discovery,
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, USA, 804-813.
arXiv,
bibtex
- J. Peters, D. Janzing, B. Schölkopf:
Causal Inference on Discrete Data using IEEEAdditive Noise Models,
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 33:2436-2450, 2011.
arXiv,
bibtex
- J. Peters, D. Janzing, B. Schölkopf:
Identifying Cause and Effect on Discrete Data using Additive Noise Models,
13th International Conference on Artificial Intelligence and Statistics (AISTATS 2010), 597-604, 2010 (conference version of TPAMI 2011).
pdf,
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.
arXiv,
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.
pdf,
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.
pdf,
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.
pdf,
bibtex
- J. Peters, D. Janzing, A. Gretton, B. Schölkopf:
Kernel Methods for Detecting the Direction of Time Series,
32nd Annual Conference of the German Classification Society (GfKl), Springer, Berlin, Germany, 57-66, 2008.
pdf,
bibtex
Book Chapters
- J. Peters, S. Bauer, N. Pfister:
Causal models for dynamical systems,
In: Probabilistic and Causal Inference: The Works of Judea Pearl,
(Ed) R. Dechter and H. Geffner and J. Halpern, ACM, New York, 2022. arXiv
- B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, J. Mooij:
Semi-supervised learning in causal and anticausal settings,
In: Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik,
(Ed) B. Schölkopf, Z. Luo, and V. Vovk, Springer-Verlag, 129-141, 2013. link
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