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

 

 

Peer-reviewed

  1. 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 (accepted), arxiv
  2. 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 2021. pdf
  3. M. Jakobsen, J. Peters:
      Distributional Robustness of K-class Estimators and the PULSE,
      The Econometrics Journal (accepted) arxiv
  4. 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) (accepted) arxiv
  5. 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
  6. 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
  7. 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
  8. N. Gnecco, N. Meinshausen, J. Peters, S. Engelke:
      Causal discovery in heavy-tailed models,
      Annals of Statistics 49(3), 1755--1778, 2021 arxiv
  9. 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
  10. S. Weichwald, J. Peters:
      Distributional robustness as a guiding principle for causality in cognitive neuroscience,
      Journal of Cognitive Neuroscience 33(2), 226--247, 2021. arxiv
  11. 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,
  12. 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
  13. 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
  14. 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
  15. 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,
  16. C. Heinze-Deml, J. Peters, N. Meinshausen:
      Invariant Causal Prediction for Nonlinear Models,
      Journal of Causal Inference 6(2), 2018. pdf, arxiv
  17. 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
  18. N. Pfister, P. Bühlmann, J. Peters:
      Invariant Causal Prediction for Sequential Data,
      Journal of the American Statistical Association 114(527), 2018. pdf, arxiv
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. D. Rothenhäusler, C. Heinze, 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
  27. 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
  28. 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
  29. J. Peters, P. Bühlmann:
      Structural Intervention Distance (SID) for Evaluating Causal Graphs,
      Neural Computation 27:771-799, 2015. journal version, arxiv, bibtex
  30. 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
  31. 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
  32. 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
  33. J. Peters, P. Bühlmann:
      Identifiability of Gaussian Structural Equation Models with Equal Error Variances,
      Biometrika 101(1):219-228, 2014. arxiv, bibtex
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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. pdf, bibtex

 

 

 

Theses

 

 

 

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