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.

76 results
(View BibTeX file of all listed publications)

**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)

**Causality: Objectives and Assessment**
In *JMLR Workshop and Conference Proceedings: Volume 6
*, pages: 1-42, (Editors: I Guyon and D Janzing and B Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (inproceedings)

**JMLR Workshop and Conference Proceedings: Volume 6**
pages: 288, MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , February 2010 (proceedings)

**Probabilistic latent variable models for distinguishing between cause and effect**
In *Advances in Neural Information Processing Systems 23*, pages: 1687-1695, (Editors: J Lafferty and CKI Williams and J Shawe-Taylor and RS Zemel and A Culotta), Curran, Red Hook, NY, USA, 24th Annual Conference on Neural Information Processing Systems (NIPS), 2010 (inproceedings)

**Distinguishing between cause and effect**
In *JMLR Workshop and Conference Proceedings: Volume 6*, pages: 147-156, (Editors: Guyon, I. , D. Janzing, B. Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop) , 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)

**Thermodynamic efficiency of information and heat flow**
*Journal of Statistical Mechanics: Theory and Experiment*, 2009(09):P09011, September 2009 (article)

**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)

**A Single-shot Measurement of the Energy of Product States in a Translation Invariant Spin Chain Can Replace Any Quantum Computation**
*New Journal of Physics*, 10(093004):1-18, September 2008 (article)

**Relating the Thermodynamic Arrow of Time to the Causal Arrow**
*Journal of Statistical Mechanics*, 2008(P04001):1-21, April 2008 (article)

**Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods**
*Neurocomputing*, 71(7-9):1248-1256, March 2008 (article)

**A Kernel-Based Causal Learning Algorithm**
In *Proceedings of the 24th International Conference on Machine Learning*, pages: 855-862, (Editors: Z Ghahramani), ACM Press, New York, NY, USA, ICML, June 2007 (inproceedings)

**Learning causality by identifying common effects with kernel-based dependence measures**
In *ESANN 2007*, pages: 453-458, D-Side, Evere, Belgium, 15th European Symposium on Artificial Neural Networks, April 2007 (inproceedings)

**Exploring the causal order of binary variables via exponential hierarchies of Markov kernels**
In *ESANN 2007*, pages: 465-470, D-Side, Evere, Belgium, 15th European Symposium on Artificial Neural Networks, April 2007 (inproceedings)

**Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions**
In *Proceedings of the 15th European Symposium on Artificial Neural Networks
*, pages: 441-446, (Editors: M Verleysen), D-Side Publications, Evere, Belgium, ESANN, April 2007 (inproceedings)

**Quantum broadcasting problem in classical low-power signal processing**
*Physical Review A*, 75(2):11, February 2007 (article)

**Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions**
NIPS Workshop on Causality and Feature Selection, December 2006 (talk)

**Causal Inference by Choosing Graphs with Most Plausible Markov Kernels**
In *Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics*, pages: 1-11, ISAIM, January 2006 (inproceedings)