Office: N4.012

Max-Planck-Ring 4

72076 Tübingen

Germany

Max-Planck-Ring 4

72076 Tübingen

Germany

+49 7071 601 564

+49 7071 601 552

Research interests:

- novel causal inference methods and their foundation
- physics of causality and information flow
- notions of complexity and their application in machine learning
- statistical methods
- statistical physics, in particular the link between causality and the second law of thermodynamics.

I founded the group "causal inference" together with Bernhard Schölkopf. The website can be found here

We (Jonas Peters, Bernhard Schölkopf, and me) have written a book on causal inference.

I have been working on quantum information theory for many years and I'm still interested in it; my current causality research is strongly influenced by the paradigm that information is physical. In 2003, I started a project on causal inference together with the student Xiaohai Sun at the Universitaet Karlsruhe (meanwhile KIT), which later resulted in a joint project with the MPI for Biological Cybernetics and thus became the beginning of the causality group. My new website can be found here.

From there you find also my complete publication list.Dominik Janzing studied physics in Tübingen (Germany) and Cork (Ireland) and received a Ph.D. in mathematics from the Unversity of Tübingen in 1998. From 1998-2006 he was a postdoc and senior scientist at the Computer Science department of the University of Karlsruhe (TH) where he worked on quantum thermodynamics, quantum control, as well as quantum complexity theory and its physical foundations. In 2006 he received his teaching permission (Habilitation) from the Computer Science Department at Universität Karlsruhe (now "Karlsruhe Institute of Technology (KIT)"). Since 2007 he has been working as a senior scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, where he founded the group causal inference together with Bernhard Schölkopf.

The group develops novel methods for causal reasoning from statistical data. These novel approaches use complexity of conditional probability distributions for causal reasoning. The idea is strongly influenced by his previous work on complexity of physical processes and the thermodynamics of information flow.

70 results
(View BibTeX file of all listed publications)

**Group invariance principles for causal generative models**
*Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS)*, 84, pages: 557-565, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, 2018 (conference)

**Detecting non-causal artifacts in multivariate linear regression models**
*Proceedings of the 35th International Conference on Machine Learning (ICML)*, 2018 (conference) Accepted

**Cause-Effect Inference by Comparing Regression Errors**
*Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) *, 84, pages: 900-909, Proceedings of Machine Learning Research, (Editors: Amos Storkey and Fernando Perez-Cruz), PMLR, 2018 (conference)

**Avoiding Discrimination through Causal Reasoning**
*Proceedings from the conference "Neural Information Processing Systems 2017.*, pages: 656-666, (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (conference)

**Causal Consistency of Structural Equation Models**
*Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI)*, (Editors: Gal Elidan, Kristian Kersting, and Alexander T. Ihler), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017, *equal contribution (conference)

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

**Detecting Confounding in Multivariate Linear Models via Spectral Analysis**
*Journal of Causal Inference*, 6(1), 2017 (article)

**Statistical Asymmetries Between Cause and Effect**
In *Time in Physics*, pages: 129-139, Tutorials, Schools, and Workshops in the Mathematical Sciences, (Editors: Renner, Renato and Stupar, Sandra), Springer International Publishing, Cham, 2017 (inbook)

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

**Identification of causal relations in neuroimaging data with latent confounders: An instrumental variable approach**
*NeuroImage*, 125, pages: 825-833, 2016 (article)

**Causal and statistical learning**
*Oberwolfach Reports*, 13(3):1896-1899, (Editors: A. Christmann and K. Jetter and S. Smale and D.-X. Zhou), 2016 (conference)

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

**Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference**
*New Journal of Phyiscs*, 18(9):093052, 2016 (article)

**Semi-Supervised Interpolation in an Anticausal Learning Scenario**
*Journal of Machine Learning Research*, 16, pages: 1923-1948, September 2015 (article)

**A quantum advantage for inferring causal structure**
*Nature Physics*, 11(5):414-420, March 2015 (article)

**Information-Theoretic Implications of Classical and Quantum Causal Structures **
18th Conference on Quantum Information Processing (QIP), 2015 (talk)

**Inference of Cause and Effect with Unsupervised Inverse Regression**
In *Proceedings of the 18th International Conference on Artificial Intelligence and Statistics*, 38, pages: 847-855, JMLR Workshop and Conference Proceedings, (Editors: Lebanon, G. and Vishwanathan, S.V.N.), JMLR.org, AISTATS, 2015 (inproceedings)

**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)

**Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Telling cause from effect in deterministic linear dynamical systems**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 285–294, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Justifying Information-Geometric Causal Inference**
In *Measures of Complexity: Festschrift for Alexey Chervonenkis*, pages: 253-265, 18, (Editors: Vovk, V., Papadopoulos, H. and Gammerman, A.), Springer, 2015 (inbook)

**Consistency of Causal Inference under the Additive Noise Model**
In *Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1)*, pages: 478-495, (Editors: Eric P. Xing and Tony Jebara), JMLR, ICML, 2014 (inproceedings)

**Estimating Causal Effects by Bounding Confounding**
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence *, pages: 240-249 , (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon , UAI, 2014 (inproceedings)

**Inferring latent structures via information inequalities **
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence*, pages: 112-121, (Editors: NL Zhang and J Tian), AUAI Press, Corvallis, Oregon, UAI, 2014 (inproceedings)

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

**Causal Inference from Passive Observations**
24th Summer School University of Jyväskylā, Finland, August, 2014 (talk)

**Replacing Causal Faithfulness with Algorithmic Independence of Conditionals**
*Minds and Machines*, 23(2):227-249, May 2013 (article)

**From Ordinary Differential Equations to Structural Causal Models: the deterministic case **
In *Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence*, pages: 440-448, (Editors: A Nicholson and P Smyth), AUAI Press, Corvallis, Oregon, UAI, 2013 (inproceedings)

**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)

**Quantifying causal influences**
*Annals of Statistics*, 41(5):2324-2358, 2013 (article)

**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)

**Thermodynamic limits of dynamic cooling**
*Physical Review E*, 84(4):16, October 2012 (article)

**Information-geometric approach to inferring causal directions**
*Artificial Intelligence*, 182-183, pages: 1-31, May 2012 (article)

**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)

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

**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)

**Testing whether linear equations are causal: A free probability theory approach**
In pages: 839-847, (Editors: Cozman, F.G. , A. Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**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)

**Finding dependencies between frequencies with the kernel cross-spectral density**
In pages: 2080-2083 , IEEE, Piscataway, NJ, USA, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2011 (inproceedings)

**On Causal Discovery with Cyclic Additive Noise Models**
In *Advances in Neural Information Processing Systems 24*, pages: 639-647, (Editors: J Shawe-Taylor and RS Zemel and PL Bartlett and FCN Pereira and KQ Weinberger), Curran Associates, Inc., Red Hook, NY, USA, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS), 2011 (inproceedings)

**Causal Inference Using the Algorithmic Markov Condition**
*IEEE Transactions on Information Theory*, 56(10):5168-5194, October 2010 (article)

**Inferring deterministic causal relations**
In *Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence*, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

**Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery**
In *Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence*, pages: 717-724, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

**Causal Markov condition for submodular information measures**
In *Proceedings of the 23rd Annual Conference on Learning Theory*, pages: 464-476, (Editors: AT Kalai and M Mohri), OmniPress, Madison, WI, USA, COLT, June 2010 (inproceedings)

**Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory**
*Open Systems and Information Dynamics*, 17(2):189-212, June 2010 (article)

**Telling cause from effect based on high-dimensional observations**
In *Proceedings of the 27th International Conference on Machine Learning*, pages: 479-486, (Editors: J Fürnkranz and T Joachims), International Machine Learning Society, Madison, WI, USA, ICML, June 2010 (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)

**On the Entropy Production of Time Series with Unidirectional Linearity**
*Journal of Statistical Physics*, 138(4-5):767-779, March 2010 (article)