**In August 2016, I have joined the statistics group in the Department of Mathematical Sciences at the University of Copenhagen as an associate professor.**

Please visit my new homepage.

32 results
(BibTeX)

**Kernel-based tests for joint independence**
*Journal of the Royal Statistical Society: Series B (Statistical Methodology)*, 2017 (article)

**Elements of Causal Inference - Foundations and Learning Algorithms**
Adaptive Computation and Machine Learning Series, The MIT Press, Cambridge, MA, USA, 2017 (book)

**The Arrow of Time in Multivariate Time Serie**
*Proceedings of the 33rd International Conference on Machine Learning*, 48, pages: 2043-2051, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M. F. and Weinberger, K. Q.), JMLR, ICML, 2016 (conference)

**Causal inference using invariant prediction: identification and confidence intervals**
*Journal of the Royal Statistical Society, Series B (Statistical Methodology)*, 78(5):947-1012, 2016, (with discussion) (article)

**Distinguishing cause from effect using observational data: methods and benchmarks**
*Journal of Machine Learning Research*, 17(32):1-102, 2016 (article)

**Modeling Confounding by Half-Sibling Regression**
*Proceedings of the National Academy of Science*, 113(27):7391-7398, 2016 (article)

**Quantifying changes in climate variability and extremes: Pitfalls and their overcoming**
*Geophysical Research Letters*, 42(22):9990-9998, November 2015 (article)

**BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions**
*Advances in Neural Information Processing Systems 28*, pages: 1513-1521, (Editors: C. Cortes, N.D. Lawrence, D.D. Lee, M. Sugiyama and R. Garnett), Curran Associates, Inc., 29th Annual Conference on Neural Information Processing Systems (NIPS), 2015 (conference)

**Removing systematic errors for exoplanet search via latent causes**
In *Proceedings of The 32nd International Conference on Machine Learning*, 37, pages: 2218–2226, JMLR Workshop and Conference Proceedings, (Editors: Bach, F. and Blei, D.), JMLR, ICML, 2015 (inproceedings)

**On the Intersection Property of Conditional Independence and its Application to Causal Discovery**
*Journal of Causal Inference*, 3(1):97-108, 2015 (article)

**Structural Intervention Distance (SID) for Evaluating Causal Graphs**
*Neural Computation *, 27(3):771-799, 2015 (article)

**Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations**
*Statistics and Computing *, 25(4):755-766, 2015 (article)

**CAM: Causal Additive Models, high-dimensional order search and penalized regression**
*Annals of Statistics*, 42(6):2526-2556, 2014 (article)

**Identifiability of Gaussian Structural Equation Models with Equal Error Variances**
*Biometrika*, 101(1):219-228, 2014 (article)

**Causal Discovery with Continuous Additive Noise Models **
*Journal of Machine Learning Research*, 15, pages: 2009-2053, 2014 (article)

**Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising**
*Journal of Machine Learning Research*, 14, pages: 3207-3260, 2013 (article)

**Semi-supervised learning in causal and anticausal settings**
In *Empirical Inference*, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

**Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders **
In *Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI)*, pages: 556-565, (Editors: A Nicholson and P Smyth), AUAI Press Corvallis, Oregon, USA, UAI, 2013 (inproceedings)

**Causal Inference on Time Series using Restricted Structural Equation Models**
In *Advances in Neural Information Processing Systems 26*, pages: 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013 (inproceedings)

**Restricted structural equation models for causal inference**
ETH Zurich, Switzerland, 2012 (phdthesis)

**On Causal and Anticausal Learning**
In *Proceedings of the 29th International Conference on Machine Learning*, pages: 1255-1262, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

**Detecting low-complexity unobserved causes**
In pages: 383-391, (Editors: FG Cozman and A Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**Identifiability of causal graphs using functional models**
In pages: 589-598, (Editors: FG Cozman and A Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**Causal Inference on Discrete Data using Additive Noise Models**
*IEEE Transactions on Pattern Analysis and Machine Intelligence*, 33(12):2436-2450, December 2011 (article)

**Kernel-based Conditional Independence Test and Application in Causal Discovery**
In pages: 804-813, (Editors: FG Cozman and A Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**Identifying Cause and Effect on Discrete Data using Additive Noise Models**
In *JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010*, pages: 597-604, (Editors: YW Teh and M Titterington), JMLR, Cambridge, MA, USA, 13th International Conference on Artificial Intelligence and Statistics, May 2010 (inproceedings)

**Kernel Methods for Detecting the Direction of Time Series **
In *Advances in Data Analysis, Data Handling and Business Intelligence*, pages: 57-66, (Editors: A Fink and B Lausen and W Seidel and A Ultsch), Springer, Berlin, Germany, 32nd Annual Conference of the Gesellschaft f{\"u}r Klassifikation e.V. (GfKl), 2010 (inproceedings)

**Detecting the Direction of Causal Time Series**
In *Proceedings of the 26th International Conference on Machine Learning*, pages: 801-808, (Editors: A Danyluk and L Bottou and ML Littman), ACM Press, New York, NY, USA, ICML, June 2009 (inproceedings)

**Nonlinear causal discovery with additive noise models**
In *Advances in neural information processing systems 21*, pages: 689-696, (Editors: D Koller and D Schuurmans and Y Bengio and L Bottou), Curran, Red Hook, NY, USA, 22nd Annual Conference on Neural Information Processing Systems (NIPS), June 2009 (inproceedings)

**Identifying confounders using additive noise models**
In *Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence*, pages: 249-257, (Editors: J Bilmes and AY Ng), AUAI Press, Corvallis, OR, USA, UAI, June 2009 (inproceedings)

**Regression by dependence minimization and its application to causal inference in additive noise models**
In *Proceedings of the 26th International Conference on Machine Learning*, pages: 745-752, (Editors: A Danyluk and L Bottou and M Littman), ACM Press, New York, NY, USA, ICML, June 2009 (inproceedings)

**Asymmetries of Time Series under Inverting their Direction**
Biologische Kybernetik, University of Heidelberg, August 2008 (diplomathesis)