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. C. Heinze-Deml, J. Peters, N. Meinshausen:
      Invariant Causal Prediction for Nonlinear Models,
      Journal of Causal Inference (accepted), 2018. arxiv
  2. 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
  3. N. Pfister, P. Bühlmann, J. Peters:
      Invariant Causal Prediction for Sequential Data,
      Journal of the American Statistical Association (accepted), 2018. arxiv
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. J. Peters, P. Bühlmann:
      Structural Intervention Distance (SID) for Evaluating Causal Graphs,
      Neural Computation 27:771-799, 2015. arxiv, bibtex
  15. 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
  16. 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
  17. 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
  18. J. Peters, P. Bühlmann:
      Identifiability of Gaussian Structural Equation Models with Equal Error Variances,
      Biometrika 101(1):219-228, 2014. arxiv, bibtex
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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. arxiv, bibtex
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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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