Senior Research Scientist

Office: 215

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+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

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. To see the publications from my previous field visit the following website

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.

60 results
(BibTeX)

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

**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)

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

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

**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)

**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)

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

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

**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)

**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)

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

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