I aim to have all papers open access. Please let me know if you find that one of the links is broken.

 

Most of the publications are also on Google Scholar.

 

Preprints

 

 

Peer-reviewed

  1. J. P. Bohl, R. R. Wood, C. Frank, P. C. Astagneau, J. Peters, M. I. Brunner:
      Hybrid models generalize better to warmer climate conditions than process-based and purely data-driven models,
      Hydrology and Earth System Sciences 30(14), 4667--4698, 2026 HESS
  2. F. Jørgensen, S. Weichwald, J. Peters:
      Unfair Utilities and First Steps Towards Improving Them,
      Journal of Causal Inference 14(1), 20250013, 2026 arXiv
  3. F. Schur, N. Pfister, P. Ding, S. Mukherjee, J. Peters:
      Many Experiments, Few Repetitions, Unpaired Data, and Sparse Effects: Is Causal Inference Possible?,
      ICML 2026 (spotlight, accepted), arXiv
  4. S. Saengkyongam, J. L. Gamella, A. C. Miller, J. Peters, N. Meinshausen, C. Heinze-Deml:
      Anti-causal domain generalization: Leveraging unlabeled data,
      ICML 2026 (accepted), arXiv
  5. P. C. Astagneau, J. Peters, S. Pool, E. Munoz-Castro, M. I. Brunner:
      RESIdual STability (RESIST) calibration for improved hydrological model time generalizability,
      Water Resources Research 62(2), 2026 ESS Open Archive 2025
  6. N. Gnecco, J. Peters, S. Engelke, and N. Pfister:
      Boosted Control Functions,
      Journal of Machine Learning Research 27(46), 1−-57, 2026 arXiv
  7. F. Schur, P. Blieske, J. Peters:
      DecoR: Deconfounding Time Series with Robust Regression,
      Journal of the Royal Statistical Society, Series B 88(3), 799--818, 2026 arXiv
  8. M. Iglesias-Alonso, F. Schur, J. von Kugelgen, J. Peters:
      Transferring Causal Effects using Proxies,
      NeurIPS 2025
  9. M. Lazzaretto, J. Peters, N. Pfister:
      Invariant Subspace Decomposition,
      Journal of Machine Learning Research 26(95), 1--56. 2025. arXiv
  10. J. Gamella, J. Peters, P. Buhlmann:
      The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology,
      Nature Machine Intelligence 7, 107--118, 2025. arXiv; also, here is a News and Views article about this paper
  11. 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
  12. 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 120(550), 1090--1101, 2024. arXiv
  13. S. Saengkyongam, E. Rosenfeld, P. Ravikumar, N. Pfister, J. Peters:
      Identifying Representations for Intervention Extrapolation,
      International Conference on Learning Representations, 2024. arXiv
  14. 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
  15. 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
  16. 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
  17. 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
  18. M. Jakobsen, R. Shah, P. Bühlmann, J. Peters:
      Structure Learning for Directed Trees,
      Journal of Machine Learning Research 23(159), 1--97, 2022. arXiv
  19. 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
  20. P. B. Mogensen, N. Thams, J. Peters:
      Invariant Ancestry Search,
      39th International Conference on Machine Learning (ICML), PMLR 162:15832--15857, 2022. arXiv
  21. 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
  22. M. Jakobsen, J. Peters:
      Distributional Robustness of K-class Estimators and the PULSE,
      The Econometrics Journal 25(2), 404--432, 2022. arXiv
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. N. Gnecco, N. Meinshausen, J. Peters, S. Engelke:
      Causal discovery in heavy-tailed models,
      Annals of Statistics 49(3), 1755--1778, 2021 arXiv
  31. 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
  32. S. Weichwald, J. Peters:
      Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness,
      Journal of Cognitive Neuroscience 33(2), 226--247, 2021. arXiv
  33. 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,
  34. 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
  35. 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
  36. 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
  37. 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,
  38. C. Heinze-Deml, J. Peters, N. Meinshausen:
      Invariant Causal Prediction for Nonlinear Models,
      Journal of Causal Inference 6(2), 2018. pdf, arXiv
  39. 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
  40. N. Pfister, P. Bühlmann, J. Peters:
      Invariant Causal Prediction for Sequential Data,
      Journal of the American Statistical Association 114(527), 2018. pdf, arXiv
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. J. Peters, P. Bühlmann:
      Structural Intervention Distance (SID) for Evaluating Causal Graphs,
      Neural Computation 27:771-799, 2015. journal version, arXiv, bibtex
  52. 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
  53. 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
  54. 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
  55. J. Peters, P. Bühlmann:
      Identifiability of Gaussian Structural Equation Models with Equal Error Variances,
      Biometrika 101(1):219-228, 2014. arXiv, bibtex
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. 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
  63. 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
  64. 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
  65. 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
  66. 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
  67. 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
  68. 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
  69. 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

    70.  

     

     

    Book Chapters

  70. 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
  71. 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