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Dominik Janzing, PD Dr.

Address:	Spemannstr. 38 72076 Tübingen
Room number:	215
Phone:	+49 7071 601 564
Fax:	+49 7071 601 552
E-Mail:	dominik.janzing

Dominik Janzing

Position: Senior Research Scientist Unit: Schölkopf

Projects
Curriculum Vitae
Publications

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 am project leader in the group ``causal inference''. 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. Since 2007 he has been working as a senior scientist at the Max Planck Institute for Biological Cybernetics in Tübingen, where he is now project leader in the group causal inference.
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.

Proceedings (1):
•	Guyon I , Janzing D and Schölkopf B : JMLR Workshop and Conference Proceedings: Volume 6, Causality: Objectives and Assessment (NIPS 2008 Workshop), 288, MIT Press, Cambridge, MA, USA, (December-2008). -
Articles (10):
•	Allahverdyan AE , Hovhannisyan KV , Janzing D and Mahler G (October-2012) Thermodynamic limits of dynamic cooling Physical Review E 84(4) 16 pages.
•	Janzing D , Mooij J , Zhang K , Lemeire J , Zscheischler J , Daniušis P , Steudel B and Schölkopf B (May-2012) Information-geometric approach to inferring causal directions Artificial Intelligence 182-183 1-31.
•	Peters J , Janzing D and Schölkopf B (December-2011) Causal Inference on Discrete Data using Additive Noise Models IEEE Transactions on Pattern Analysis and Machine Intelligence 33(12) 2436-2450.
•	Janzing D and Schölkopf B (October-2010) Causal Inference Using the Algorithmic Markov Condition IEEE Transactions on Information Theory 56(10) 5168-5194.
•	Janzing D and Steudel B (June-2010) Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory Open Systems and Information Dynamics 17(2) 189-212.
•	Janzing D (March-2010) On the Entropy Production of Time Series with Unidirectional Linearity Journal of Statistical Physics 138(4-5) 767-779.
•	Allahverdyan AE , Janzing D and Mahler G (September-2009) Thermodynamic efficiency of information and heat flow Journal of Statistical Mechanics: Theory and Experiment 2009(P09011) 1-35.
•	Janzing D , Wocjan P and Zhang S (September-2008) 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.
•	Allahverdyan AE and Janzing D (April-2008) Relating the Thermodynamic Arrow of Time to the Causal Arrow Journal of Statistical Mechanics 2008(P04001) 1-21.
•	Sun X , Janzing D and Schölkopf B (March-2008) Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods Neurocomputing 71(7-9) 1248-1256.
Conference papers (24):
•	Mooij J , Janzing D , Schölkopf B and Heskes T (January-2012) On Causal Discovery with Cyclic Additive Noise Models In: Advances in Neural Information Processing Systems 24, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), 639-647.
•	Janzing D , Sgouritsa E , Stegle O , Peters J and Schölkopf B (July-2011) Detecting low-complexity unobserved causes 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 383-391.
•	Peters J , Mooij J , Janzing D and Schölkopf B (July-2011) Identifiability of causal graphs using functional models 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 589-598.
•	Zhang K , Peters J , Janzing D and Schölkopf B (July-2011) Kernel-based Conditional Independence Test and Application in Causal Discovery 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 804-813.
•	Zscheischler J , Janzing D and Zhang K (July-2011) Testing whether linear equations are causal: A free probability theory approach 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 839-847.
•	Mooij JM , Stegle O , Janzing D , Zhang K and Schölkopf B (June-2011) Probabilistic latent variable models for distinguishing between cause and effect In: Advances in Neural Information Processing Systems 23, Twenty-Fourth Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 1687-1695.
•	Besserve M , Janzing D , Logothetis NK and Schölkopf B (May-2011) Finding dependencies between frequencies with the kernel cross-spectral density IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), IEEE, Piscataway, NJ, USA, 2080-2083.
•	Daniusis P , Janzing D , Mooij J , Zscheischler J , Steudel B , Zhang K and Schölkopf B (July-2010) Inferring deterministic causal relations 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), AUAI Press, Corvallis, OR, USA, 143-150.
•	Zhang K , Schölkopf B and Janzing D (July-2010) Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), AUAI Press, Corvallis, OR, USA, 717-724.
•	Steudel B , Janzing D and Schölkopf B (June-2010) Causal Markov condition for submodular information measures 23rd Annual Conference on Learning Theory (COLT 2010), OmniPress, Madison, WI, USA, 464-476.
•	Janzing D , Hoyer P and Schölkopf B (June-2010) Telling cause from effect based on high-dimensional observations 27th International Conference on Machine Learning (ICML 2010), International Machine Learning Society, Madison, WI, USA, 479-486.
•	Peters J , Janzing D and Schölkopf B (May-2010) Identifying Cause and Effect on Discrete Data using Additive Noise Models In: JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR, Cambridge, MA, USA, 597-604.
•	Mooij J and Janzing D (2010) Distinguishing between cause and effect In: JMLR Workshop and Conference Proceedings: Volume 6, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 147-156.
•	Peters J , Janzing D , Gretton A and Schölkopf B (2010) Kernel Methods for Detecting the Direction of Time Series In: Advances in Data Analysis, Data Handling and Business Intelligence, 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008), Springer, Berlin, Germany, 57-66.
•	Peters J , Janzing D , Gretton A and Schölkopf B (June-2009) Detecting the Direction of Causal Time Series In: ICML 2009, 26th International Conference on Machine Learning, ACM Press, New York, NY, USA, 801-808.
•	Janzing D , Peters J , Mooij JM and Schölkopf B (June-2009) Identifying confounders using additive noise models 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, Corvallis, OR, USA, 249-257.
•	Hoyer PO , Janzing D , Mooij JM , Peters J and Schölkopf B (June-2009) Nonlinear causal discovery with additive noise models In: Advances in neural information processing systems 21, Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 689-696.
•	Mooij JM , Janzing D , Peters J and Schölkopf B (June-2009) Regression by dependence minimization and its application to causal inference in additive noise models In: ICML 2009, 26th International Conference on Machine Learning, ACM Press, New York, NY, USA, 745-752.
•	Guyon I , Janzing D and Schölkopf B (December-2008) Causality: Objectives and Assessment In: JMLR Workshop and Conference Proceedings: Volume 6, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 1-42.
•	Sun X , Janzing D , Schölkopf B and Fukumizu K (June-2007) A Kernel-Based Causal Learning Algorithm In: ICML 2007, 24th Annual International Conference on Machine Learning, ACM Press, New York, NY, USA, 855-862.
•	Sun X , Janzing D and Schölkopf B (April-2007) Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions In: ESANN 2007, 15th European Symposium on Artificial Neural Networks, D-Side Publications, Evere, Belgium, 441-446.
•	Sun X and Janzing D (April-2007) Exploring the causal order of binary variables via exponential hierarchies of Markov kernels In: ESANN 2007, 15th European Symposium on Artificial Neural Networks, D-Side, Evere, Belgium, 465-470.
•	Sun X and Janzing D (April-2007) Learning causality by identifying common effects with kernel-based dependence measures In: ESANN 2007, 15th European Symposium on Artificial Neural Networks, D-Side, Evere, Belgium, 453-458.
•	Sun X , Janzing D and Schölkopf B (January-2006) Causal Inference by Choosing Graphs with Most Plausible Markov Kernels In: AI & Math 2006, Ninth International Symposium on Artificial Intelligence and Mathematics, 1-11.
Talks (1):
•	Sun X , Janzing D and Schölkopf B (December-2006): Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions, NIPS 2006 Workshop on Causality and Feature Selection, Whistler, BC, Canada.