Jonas Martin Peters

Research output

Research output per year 2009 2009 2011 2015 2016 2019

• 19 Journal article
• 13 Book chapter
• 2 Article in proceedings
• 1 Book

Research output per year

Research output per year

Search results

• 2019

  Inferring causation from time series in Earth system sciences

  Runge, J., Bathiany, S., Bollt, E., Camps-Valls, G., Coumou, D., Deyle, E., Glymour, C., Kretschmer, M., Mahecha, M. D., Muñoz-Marí, J., van Nes, E. H., Peters, J., Quax, R., Reichstein, M., Scheffer, M., Schölkopf, B., Spirtes, P., Sugihara, G., Sun, J., Zhang, K., & 1 othersZscheischler, J., 2019, In: Nature Communications. 10, 1, 13 p., 2553.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Earth Sciences 100%
  • Dynamical System 79%
  • Causality 48%
  • photographic developers 33%
  • inference 28%
  75 Citations (Scopus)

  22 Downloads (Pure)

• Invariant Causal Prediction for Sequential Data

  Pfister, N., Bühlmann, P. & Peters, J., 2019, In: Journal of the American Statistical Association. 114, 527, p. 1264-1276 13 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Predictors 100%
  • Prediction 76%
  • Invariant 52%
  • Monetary Policy 35%
  • Granger Causality 28%
  8 Citations (Scopus)
• 2018

  Invariant Causal Prediction for Nonlinear Models

  Heinze-Deml, C., Peters, J. & Meinshausen, N., Sept 2018, In: Journal of Causal Inference. 6, 2, 35 p., 20170016.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Nonlinear Model 100%
  • Prediction 84%
  • Invariant 57%
  • Nonparametric Test 45%
  • Population Projections 38%
  19 Citations (Scopus)

• Invariant Models for Causal Transfer Learning

  Rojas-Carulla, M., Schoelkopf, B., Turner, R. & Peters, J., 1 Sept 2018, In: Journal of Machine Learning Research. 19, 1, p. 1-34

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Transfer Learning 100%
  • Transfer learning 63%
  • Subset 29%
  • Invariant 29%
  • Genes 25%
  20 Citations (Scopus)

  9 Downloads (Pure)

• Kernel-based tests for joint independence

  Pfister, N., Bühlmann, P., Schölkopf, B. & Peters, J., 1 Jan 2018, In: Journal of the Royal Statistical Society, Series B (Statistical Methodology). 80, 1, p. 5-31 27 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Kernel 100%
  • Independence 72%
  • kernel 56%
  • Testing 34%
  • Hilbert Space 32%
  40 Citations (Scopus)
• 2017

  Elements of Causal Inference: Foundations and Learning Algorithms: Foundations and Learning Algorithms

  Peters, J. M., Janzing, D. & Schölkopf, B., 2017, MIT Press. 288 p. (Adaptive Computation and Machine Learning series ).

  Research output: Book/Report › Book › Research › peer-review

  • Data Science 100%
  • Learning algorithms 61%
  • Machine learning 54%
• 2016

  Causal inference by using invariant prediction: identification and confidence intervals

  Peters, J. M., Bühlmann, P. & Meinshausen, N., 1 Nov 2016, In: Journal of the Royal Statistical Society, Series B (Statistical Methodology). 78, 5, p. 947-1012

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Confidence Interval 100%
  • Causal Model 96%
  • Causal Inference 92%
  • Confidence interval 56%
  • Prediction 55%

• Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks

  Mooij, J. M., Peters, J. M., Janzing, D., Zscheischler, J. & Schölkopf, B., 1 Apr 2016, In: Journal of Machine Learning Research. 17, p. 1-102

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Additive noise 100%
  • Benchmark 83%
  • Meteorology 61%
  • Medicine 43%
  • Feedback 27%
  116 Citations (Scopus)

• Methods for causal inference from gene perturbation experiments and validation

  Meinshausen, N., Hauser, A., Mooij, J. M., Peters, J., Versteeg, P. & Bühlmann, P., 5 Jul 2016, In: Proceedings of the National Academy of Sciences of the United States of America. 113, 27, p. 7361-7368

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Genes 100%
  • Gene Deletion 67%
  • Saccharomyces cerevisiae 57%
  • Software 46%
  • Genome 42%
  34 Citations (Scopus)

• Modeling confounding by half-sibling regression

  Schölkopf, B., Hogg, D. W., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C-J. & Peters, J., 5 Jul 2016, In: Proceedings of the National Academy of Sciences of the United States of America. 113, 27, p. 7391-7398 8 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Astronomy 100%
  • Noise 50%
  12 Citations (Scopus)

• The Arrow of Time in Multivariate Time Series

  Bauer, S., Schölkopf, B. & Peters, J. M., 2016, Proceedings of the 33rd International Conference on Machine Learning (ICML). p. 2043-2051 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Multivariate Time Series 100%
  • VARMA Models 33%
  • Innovation 28%
  • Sample Size 26%
  • Causal Inference 24%
• 2015

  BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

  Rothenhäusler, D., Heinze, C., Peters, J. M. & Meinshausen, N., 2015, Advances in Neural Information Processing Systems 28 (NIPS 2015): Proceedings from the conference, "Neural Information Processing Systems 2015." •. C. Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M. & Garnett, R. (eds.). NIPS Proceedings, p. 1513-1521 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

  • Learning 100%
  • Unknown 71%
  • Graph in graph theory 50%
  • Flow Cytometry 35%
  • Financial Time Series 29%
  12 Citations (Scopus)

• Causal inference using invariant prediction: identification and confidence intervals

  Peters, J. M., Bühlmann, P. & Meinshausen, N., 2015, In: Journal of the Royal Statistical Society, Series B (Statistical Methodology). 78, A, p. 1-42

  Research output: Contribution to journal › Journal article › Research › peer-review

• Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations

  Schölkopf, B., Muandet, K., Fukumizu, K., Harmeling, S. & Peters, J. M., 26 Jul 2015, In: Statistics and Computing. 25, 4, p. 755-766 12 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Hilbert Space 100%
  • Variable Kernel 95%
  • Kernel 76%
  • Reproducing Kernel Hilbert Space 71%
  • Random Variables 63%
  17 Citations (Scopus)

• On the Intersection Property of Conditional Independence and its Application to Causal Discovery

  Peters, J. M., Mar 2015, In: Journal of Causal Inference. 3, 1, p. 97-108

  Research output: Contribution to journal › Journal article › Research › peer-review

• Quantifying changes in climate variability and extremes: Pitfalls and their overcoming

  Sippel, S., Zscheischler, J., Heimann, M., Otto, F. E. L., Peters, J. M. & Mahecha, M. D., 28 Nov 2015, In: Geophysical Research Letters (Online). 42, 22, p. 9990-9998

  Research output: Contribution to journal › Journal article › Research › peer-review

  • climate 100%
  • artifact 50%
  • normalization 41%
  • artifacts 36%
  • temperature 29%
  39 Citations (Scopus)

• Removing systematic errors for exoplanet search via latent causes

  Schölkopf, B., Hogg, D. W., Wang, D., Foreman-Mackey, D., Janzing, D., Simon-Gabriel, C-J. & Peters, J. M., 2015, Proceedings of the 32nd International Conference on Machine Learning (ICML). p. 2218-2226 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  4 Citations (Scopus)

• Structural intervention distance for evaluating causal graphs

  Peters, J. M. & Bühlmann, P., 26 Mar 2015, In: Neural Computation. 27, 3, p. 771-799 29 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Graph 100%
  • Causal 76%
  • Causal Inference 75%
  • Software 73%
  • Closeness 21%
  11 Citations (Scopus)
• 2014

  CAM: Causal additive models, high-dimensional order search and penalized regression

  Buehlmann, P., Peters, J. M. & Ernest, J., 1 Dec 2014, In: Annals of Statistics. 42, 6, p. 2526-2556

  Research output: Contribution to journal › Journal article › Research › peer-review

  • Causal Model 100%
  • Penalized Regression 95%
  • Additive Models 93%
  • Maximum Likelihood Estimation 65%
  • High-dimensional 57%
  58 Citations (Scopus)

• Causal Discovery with Continuous Additive Noise Models

  Peters, J., Mooij, J. M., Janzing, D. & Schölkopf, B., Jun 2014, In: Journal of Machine Learning Research. 15, p. 2009-2053 45 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  114 Citations (Scopus)

• Identifiability of Gaussian Structural Equation Models with Equal Error Variances

  Peters, J. & Bühlmann, P., Mar 2014, In: Biometrika. 101, 1, p. 219-228 10 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  60 Citations (Scopus)
• 2013

  Causal Inference on Time Series using Structural Equation Models

  Peters, J. M., Janzing, D. & Schölkopf, B., 2013, Advances in Neural Information Processing Systems 25 (NIPS). p. 585-592 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

• Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising

  Bottou, L., Peters, J. M., Quiñonero-Candela, J., Charles, D. X., Chickering, D. M., Portugualy, E., Ray, D., Simard, P. & Snelson, E., Nov 2013, In: Journal of Machine Learning Research. 14, p. 3207-3260 54 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  178 Citations (Scopus)

• Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders

  Sgouritsa, E., Janzing, D., Peters, J. M. & Schölkopf, B., 2013, Proceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI). p. 556-565 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  4 Citations (Scopus)
• 2012

  On causal and anticausal learning

  Schölkopf, B., Janzing, D., Peters, J. M., Sgouritsa, E., Zhang, K. & Mooij, J. M., 2012, Proceedings of the 29th International Conference on Machine Learning (ICML). p. 1255-1262 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  88 Citations (Scopus)
• 2011

  Causal Inference on Discrete Data Using Additive Noise Models

  Peters, J. M., Janzing, D. & Schölkopf, B., 2011, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 33, p. 2436-2450 15 p.

  Research output: Contribution to journal › Journal article › Research › peer-review

  79 Citations (Scopus)

• Detecting low-complexity unobserved causes

  Janzing, D., Sgouritsa, E., Stegle, O., Peters, J. M. & Schölkopf, B., 2011, Proceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI). p. 383-391 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  8 Citations (Scopus)

• Identifiability of Causal Graphs using Functional Models

  Peters, J. M., Mooij, J. M., Janzing, D. & Schölkopf, B., 2011, Proceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI). p. 589-598 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  36 Citations (Scopus)

• Kernel-based Conditional Independence Test and Application in Causal Discovery

  Zhang, K., Peters, J. M., Janzing, D. & Schölkopf, B., 2011, Proceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI). p. 804-813 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  119 Citations (Scopus)
• 2010

  Identifying Cause and Effect on Discrete Data using Additive Noise Models

  Peters, J. M., Janzing, D. & Schölkopf, B., 2010, AIStats 13. Journal of Machine Learning Research: Workshop and Conference Proceedings, Vol. 9. p. 597-604 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  31 Citations (Scopus)
• 2009

  Detecting the Direction of Causal Time Series

  Peters, J. M., Janzing, D., Gretton, A. & Schölkopf, B., 2009, Proceedings of the 26th International Conference on Machine Learning (ICML). p. 801-808 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  18 Citations (Scopus)

• Identifying confounders using additive noise models

  Janzing, D., Peters, J., Mooij, J. M. & Schölkopf, B., 2009, Proceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence (UAI ). p. 249-257 9 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  25 Citations (Scopus)

• Kernel methods for detecting the direction of time series

  Peters, J. M., Janzing, D., Gretton, A. & Schölkopf, B., 2009, Proccedings of the 32nd Annual Conference of the German Classification Society (GfKl 2008). p. 1-10 10 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

• Nonlinear causal discovery with additive noise models

  Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J. & Schölkopf, B., 2009, Advances in Neural Information Processing Systems 21 (NIPS). p. 689-696 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

  291 Citations (Scopus)

• Regression by Dependence Minimization and its Application to Causal Inference

  Mooij, J. M., Janzing, D., Peters, J. M. & Schölkopf, B., 2009, Proceedings of the 26th International Conference on Machine Learning (ICML). p. 745-752 8 p.

  Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review