Jonas Peters
Jonas Peters
Associate Professor for Statistics, University of Copenhagen
Verified email at math.ku.dk - Homepage
TitleCited byYear
Nonlinear causal discovery with additive noise models
PO Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf
Advances in neural information processing systems, 689-696, 2009
3772009
Kernel-based conditional independence test and application in causal discovery
K Zhang, J Peters, D Janzing, B Schölkopf
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI …, 2012
2162012
Counterfactual reasoning and learning systems: The example of computational advertising
L Bottou, J Peters, J Quiñonero-Candela, DX Charles, DM Chickering, ...
The Journal of Machine Learning Research 14 (1), 3207-3260, 2013
1932013
Distinguishing cause from effect using observational data: methods and benchmarks
JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf
The Journal of Machine Learning Research 17 (1), 1103-1204, 2016
1582016
Causal discovery with continuous additive noise models
J Peters, JM Mooij, D Janzing, B Schölkopf
The Journal of Machine Learning Research 15, 2009-2053, 2014
1452014
On causal and anticausal learning
B Schölkopf, D Janzing, J Peters, E Sgouritsa, K Zhang, J Mooij
29th International Conference on Machine Learning (ICML 2012), 1255-1262, 2012, 2012
1192012
Causal inference on discrete data using additive noise models
J Peters, D Janzing, B Scholkopf
IEEE Transactions on Pattern Analysis and Machine Intelligence 33 (12), 2436 …, 2011
1022011
Elements of causal inference: foundations and learning algorithms
J Peters, D Janzing, B Schölkopf
MIT press, 2017
982017
Causal inference by using invariant prediction: identification and confidence intervals
J Peters, P Bühlmann, N Meinshausen
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2016
902016
Regression by dependence minimization and its application to causal inference in additive noise models
J Mooij, D Janzing, J Peters, B Schölkopf
26th annual international conference on machine learning (ICML), 745-752, 2009
782009
Identifiability of Gaussian structural equation models with equal error variances
J Peters, P Bühlmann
Biometrika 101 (1), 219-228, 2013
73*2013
Identifiability of causal graphs using functional models
J Peters, J Mooij, D Janzing, B Schölkopf
27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI …, 2012
722012
CAM: Causal additive models, high-dimensional order search and penalized regression
P Bühlmann, J Peters, J Ernest
The Annals of Statistics 42 (6), 2526-2556, 2014
652014
Identifying cause and effect on discrete data using additive noise models
J Peters, D Janzing, B Schölkopf
13th International Conference on Artificial Intelligence and Statistics, 597-604, 2010
432010
Causal inference on time series using restricted structural equation models
J Peters, D Janzing, B Schölkopf
Advances in Neural Information Processing Systems, 154-162, 2013
402013
Kernel‐based tests for joint independence
N Pfister, P Bühlmann, B Schölkopf, J Peters
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2018
342018
Identifying confounders using additive noise models
D Janzing, J Peters, J Mooij, B Schölkopf
25th Conference on Uncertainty in Artificial Intelligence (UAI), 249-257, 2009
322009
Methods for causal inference from gene perturbation experiments and validation
N Meinshausen, A Hauser, JM Mooij, J Peters, P Versteeg, P Bühlmann
Proceedings of the National Academy of Sciences 113 (27), 7361-7368, 2016
292016
Detecting the direction of causal time series
J Peters, D Janzing, A Gretton, B Schölkopf
26th annual international conference on machine learning (ICML), 801-808, 2009
272009
Quantifying changes in climate variability and extremes: Pitfalls and their overcoming
S Sippel, J Zscheischler, M Heimann, FEL Otto, J Peters, MD Mahecha
Geophysical Research Letters 42 (22), 9990-9998, 2015
262015
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