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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
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Picture of Janzing, Dominik, PD Dr.

Dominik Janzing

Position: Senior Research Scientist  Unit: Schölkopf

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. 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.

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Proceedings (1):

• Guyon I , Janzing D Person and Schölkopf B Person: JMLR Workshop and Conference Proceedings: Volume 6, Causality: Objectives and Assessment (NIPS 2008 Workshop), 288, MIT Press, Cambridge, MA, USA, (2010).

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Articles (13):

• Allahverdyan AE , Hovhannisyan KV , Janzing D Person and Mahler G (2012) Thermodynamic limits of dynamic cooling Physical Review E 84(4) 16 pages.
• Lemeire J and Janzing D Person (2012) Replacing Causal Faithfulness with Algorithmic Independence of Conditionals Minds and Machines 1-23.
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• Janzing D Person, Mooij J Person, Zhang K Person, Lemeire J , Zscheischler J Person, Daniušis P Person, Steudel B Person and Schölkopf B Person (2012) Information-geometric approach to inferring causal directions Artificial Intelligence 182-183 1-31.
• Janzing D Person, Balduzzi D Person, Grosse-Wentrup M Person and Schölkopf B Person (2012) Quantifying causal influences Annals of Statistics . submitted
• Peters J Person, Janzing D Person and Schölkopf B Person (2011) Causal Inference on Discrete Data using Additive Noise Models IEEE Transactions on Pattern Analysis and Machine Intelligence 33(12) 2436-2450.
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• Janzing D Person and Schölkopf B Person (2010) Causal Inference Using the Algorithmic Markov Condition IEEE Transactions on Information Theory 56(10) 5168-5194.
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• Janzing D Person and Steudel B Person (2010) Justifying Additive Noise Model-Based Causal Discovery via Algorithmic Information Theory Open Systems and Information Dynamics 17(2) 189-212.
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• Janzing D Person (2010) On the Entropy Production of Time Series with Unidirectional Linearity Journal of Statistical Physics 138(4-5) 767-779.
• Allahverdyan AE , Janzing D Person and Mahler G (2009) Thermodynamic efficiency of information and heat flow Journal of Statistical Mechanics: Theory and Experiment 2009(P09011) 1-35.
• Janzing D Person, Wocjan P and Zhang S (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 Person (2008) Relating the Thermodynamic Arrow of Time to the Causal Arrow Journal of Statistical Mechanics 2008(P04001) 1-21.
• Sun X Person, Janzing D Person and Schölkopf B Person (2008) Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods Neurocomputing 71(7-9) 1248-1256.
• Janzing D Person and Steudel B Person (2007) Quantum broadcasting problem in classical low-power signal processing Physical Review A 75(2) 11 pages.

Conference papers (27):

• Sgouritsa E Person, Janzing D Person, Peters J Person and Schölkopf B Person (2013) In: Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders, 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013). accepted
• Mooij J Person, Janzing D Person and Schölkopf B Person (2013) From Ordinary Differential Equations to Structural Causal Models: the deterministic case 29th Conference on Uncertainty in Artificial Intelligence (UAI 2013). accepted
• Schölkopf B Person, Janzing D Person, Peters J Person, Sgouritsa E Person, Zhang K Person and Mooij J Person (2012) On Causal and Anticausal Learning In: Proceedings of the 29th International Conference on Machine Learning (ICML 2012), (Ed) J Langford and J Pineau, 29th International Conference on Machine Learning (ICML 2012), Omnipress, New York, NY, USA, 1255-1262.
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• Janzing D Person, Sgouritsa E Person, Stegle O Person, Peters J Person and Schölkopf B Person (2011) Detecting low-complexity unobserved causes (Ed) FG Cozman and A Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 383-391.
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• Peters J Person, Mooij J Person, Janzing D Person and Schölkopf B Person (2011) Identifiability of causal graphs using functional models (Ed) FG Cozman and A Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 589-598.
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• Zhang K Person, Peters J Person, Janzing D Person and Schölkopf B Person (2011) Kernel-based Conditional Independence Test and Application in Causal Discovery (Ed) FG Cozman and A Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 804-813.
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• Zscheischler J Person, Janzing D Person and Zhang K Person (2011) Testing whether linear equations are causal: A free probability theory approach (Ed) Cozman, F.G. , A. Pfeffer, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), AUAI Press, Corvallis, OR, USA, 839-847.
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• Besserve M Person, Janzing D Person, Logothetis NK Person and Schölkopf B Person (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.
• Mooij J Person, Janzing D Person, Schölkopf B Person and Heskes T (2011) On Causal Discovery with Cyclic Additive Noise Models In: Advances in Neural Information Processing Systems 24, (Ed) J Shawe-Taylor, RS Zemel, PL Bartlett, FCN Pereira and KQ Weinberger, Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011), Curran Associates, Inc., Red Hook, NY, USA, 639-647.
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• Daniusis P Person, Janzing D Person, Mooij J Person, Zscheischler J Person, Steudel B Person, Zhang K Person and Schölkopf B Person (2010) Inferring deterministic causal relations In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, UAI 2010, AUAI Press, Corvallis, OR, USA, 143-150.
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• Zhang K Person, Schölkopf B Person and Janzing D Person (2010) Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, UAI 2010, AUAI Press, Corvallis, OR, USA, 717-724.
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• Steudel B Person, Janzing D Person and Schölkopf B Person (2010) Causal Markov condition for submodular information measures In: Proceedings of the 23rd Annual Conference on Learning Theory, (Ed) AT Kalai and M Mohri, COLT 2010, OmniPress, Madison, WI, USA, 464-476.
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• Janzing D Person, Hoyer P and Schölkopf B Person (2010) Telling cause from effect based on high-dimensional observations In: Proceedings of the 27th International Conference on Machine Learning, (Ed) J Fürnkranz and T Joachims, ICML 2010, International Machine Learning Society, Madison, WI, USA, 479-486.
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• Peters J Person, Janzing D Person and Schölkopf B Person (2010) Identifying Cause and Effect on Discrete Data using Additive Noise Models In: JMLR Workshop and Conference Proceedings Volume 9: AISTATS 2010, (Ed) YW Teh and M Titterington, 13th International Conference on Artificial Intelligence and Statistics, JMLR, Cambridge, MA, USA, 597-604.
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• Guyon I , Janzing D Person and Schölkopf B Person (2010) Causality: Objectives and Assessment In: JMLR Workshop and Conference Proceedings: Volume 6, (Ed) I Guyon, D Janzing and B Schölkopf, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 1-42.
• Mooij J Person and Janzing D Person (2010) Distinguishing between cause and effect In: JMLR Workshop and Conference Proceedings: Volume 6, (Ed) Guyon, I. , D. Janzing, B. Schölkopf, Causality: Objectives and Assessment (NIPS 2008 Workshop), MIT Press, Cambridge, MA, USA, 147-156.
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• Peters J Person, Janzing D Person, Gretton A Person and Schölkopf B Person (2010) Kernel Methods for Detecting the Direction of Time Series In: Advances in Data Analysis, Data Handling and Business Intelligence, (Ed) A Fink, B Lausen, W Seidel and A Ultsch, 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008), Springer, Berlin, Germany, 57-66.
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• Mooij JM Person, Stegle O Person, Janzing D Person, Zhang K Person and Schölkopf B Person (2010) Probabilistic latent variable models for distinguishing between cause and effect In: Advances in Neural Information Processing Systems 23, (Ed) J Lafferty, CKI Williams, J Shawe-Taylor, RS Zemel and A Culotta, 24th Annual Conference on Neural Information Processing Systems (NIPS 2010), Curran, Red Hook, NY, USA, 1687-1695.
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• Peters J Person, Janzing D Person, Gretton A Person and Schölkopf B Person (2009) Detecting the Direction of Causal Time Series In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk, L Bottou and ML Littman, ICML 2009, ACM Press, New York, NY, USA, 801-808.
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• Janzing D Person, Peters J Person, Mooij JM Person and Schölkopf B Person (2009) Identifying confounders using additive noise models In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, (Ed) J Bilmes and AY Ng, UAI 2009, AUAI Press, Corvallis, OR, USA, 249-257.
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• Hoyer PO , Janzing D Person, Mooij JM Person, Peters J Person and Schölkopf B Person (2009) Nonlinear causal discovery with additive noise models In: Advances in neural information processing systems 21, (Ed) D Koller, D Schuurmans, Y Bengio and L Bottou, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008), Curran, Red Hook, NY, USA, 689-696.
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• Mooij JM Person, Janzing D Person, Peters J Person and Schölkopf B Person (2009) Regression by dependence minimization and its application to causal inference in additive noise models In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk, L Bottou and M Littman, ICML 2009, ACM Press, New York, NY, USA, 745-752.
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• Sun X Person, Janzing D Person, Schölkopf B Person and Fukumizu K Person (2007) A Kernel-Based Causal Learning Algorithm In: Proceedings of the 24th International Conference on Machine Learning(ICML 2007), (Ed) Z Ghahramani, ICML 2007, ACM Press, New York, NY, USA, 855-862.
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• Sun X Person, Janzing D Person and Schölkopf B Person (2007) Distinguishing Between Cause and Effect via Kernel-Based Complexity Measures for Conditional Distributions In: Proceedings of the 15th European Symposium on Artificial Neural Networks, (Ed) M Verleysen, ESANN 2007, D-Side Publications, Evere, Belgium, 441-446.
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• Sun X Person and Janzing D Person (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.
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• Sun X Person and Janzing D Person (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.
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• Sun X Person, Janzing D Person and Schölkopf B Person (2006) Causal Inference by Choosing Graphs with Most Plausible Markov Kernels In: Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, ISAIM 2006, 1-11.
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Talks (1):

• Sun X Person, Janzing D Person and Schölkopf B Person (2006): Inferring Causal Directions by Evaluating the Complexity of Conditional Distributions, NIPS 2006 Workshop on Causality and Feature Selection, Whistler, BC, Canada.

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