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 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.

56 results
(BibTeX)

**Removing systematic errors for exoplanet search via latent causes** In: *Proceedings of The 32nd International Conference on Machine Learning*, 37, 2218–2226, JMLR, ICML 2015
(In Proceedings)

**Modeling Confounding by Half-Sibling Regression**(Article)

**Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components** In: *Proceedings of the 32nd International Conference on Machine Learning*, 37, 1917–1925, JMLR, ICML 2015
(In Proceedings)

**Telling cause from effect in deterministic linear dynamical systems** In: *Proceedings of the 32nd International Conference on Machine Learning*, 37, 285–294, JMLR, ICML 2015
(In Proceedings)

**Inference of Cause and Effect with Unsupervised Inverse Regression** In: *Proceedings of the 18th International Conference on Artificial Intelligence and Statistics*, JMLR.org, AISTATS 2015
(In Proceedings)

**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
(Miscellaneous)

**Consistency of Causal Inference under the Additive Noise Model** In: *Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1)*, 478-495, JMLR, ICML 2014
(In Proceedings)

**Estimating Causal Effects by Bounding Confounding** In: *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence *, 240-249 , AUAI Press Corvallis, Oregon , UAI 2014
(In Proceedings)

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

**Causal Discovery with Continuous Additive Noise Models ** *Journal of Machine Learning Research*, 15, 2009-2053
(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*, 440-448, AUAI Press, Corvallis, Oregon, UAI 2013
(In Proceedings)

**Semi-supervised learning in causal and anticausal settings** In: *Empirical Inference*, 129–141, Springer-Verlag
(Book Chapter)

**Quantifying causal influences** *Annals of Statistics*, 41(5):2324-2358
(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)*, 556-565, AUAI Press Corvallis, Oregon, USA, UAI 2013
(In Proceedings)

**Causal Inference on Time Series using Restricted Structural Equation Models** In: *Advances in Neural Information Processing Systems 26*, 154-162, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)
(In Proceedings)

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

**Information-geometric approach to inferring causal directions** *Artificial Intelligence*, 182-183, 1-31
(Article)

**Thermodynamic limits of dynamic cooling** *Physical Review E*, 84(4):16 pages
(Article)

Office: 215

Spemannstr. 38

72076 Tübingen

Spemannstr. 38

72076 Tübingen

+49 7071 601 564

+49 7071 601 552