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Jonas Peters
Jonas Peters
Professor of Statistics, ETH Zurich
Zweryfikowany adres z stat.math.ethz.ch - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
Elements of causal inference: foundations and learning algorithms
J Peters, D Janzing, B Schölkopf
The MIT Press, 2017
22502017
Nonlinear causal discovery with additive noise models
P Hoyer, D Janzing, JM Mooij, J Peters, B Schölkopf
Advances in neural information processing systems 21, 2008
11942008
Causal inference using invariant prediction: identification and confidence intervals
J Peters, P Bühlmann, N Meinshausen
Journal of the Royal Statistical Society, Series B (with discussion) 78 (5 …, 2016
10332016
Counterfactual reasoning and learning systems: The example of computational advertising
L Bottou, J Peters, J Quiñonero-Candela, D Charles, M Chickering, ...
Journal of Machine Learning Research 14 (Léon Bottou, Jonas Peters, Joaquin …, 2013
8142013
Inferring causation from time series in Earth system sciences
J Runge, S Bathiany, E Bollt, G Camps-Valls, D Coumou, E Deyle, ...
Nature communications 10 (1), 2553, 2019
7522019
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
7042012
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
6682012
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
5972014
Distinguishing cause from effect using observational data: methods and benchmarks
JM Mooij, J Peters, D Janzing, J Zscheischler, B Schölkopf
Journal of Machine Learning Research 17 (32), 1-102, 2016
5632016
Invariant models for causal transfer learning
M Rojas-Carulla, B Schölkopf, R Turner, J Peters
Journal of Machine Learning Research 19 (36), 1-34, 2018
3762018
Identifiability of Gaussian structural equation models with equal error variances
J Peters, P Bühlmann
Biometrika 101 (1), 219-228, 2014
3592014
The hardness of conditional independence testing and the generalised covariance measure
RD Shah, J Peters
3362020
CAM: Causal additive models, high-dimensional order search and penalized regression
P Bühlmann, J Peters, J Ernest
3322014
Invariant causal prediction for nonlinear models
C Heinze-Deml, J Peters, N Meinshausen
Journal of Causal Inference 6 (2), 20170016, 2018
2812018
Anchor regression: Heterogeneous data meet causality
D Rothenhäusler, N Meinshausen, P Bühlmann, J Peters
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021
2242021
Causal inference on time series using restricted structural equation models
J Peters, D Janzing, B Schölkopf
Advances in neural information processing systems 26, 2013
2182013
Kernel-based tests for joint independence
N Pfister, P Bühlmann, B Schölkopf, J Peters
Journal of Royal Statistical Society, Series B 80, 5-31, 2017
2042017
Foundations of structural causal models with cycles and latent variables
S Bongers, P Forré, J Peters, JM Mooij
The Annals of Statistics 49 (5), 2885-2915, 2021
196*2021
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
1962011
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
1762012
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