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 19982006 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.

E. Sgouritsa, D. Janzing, P. Hennig, B. Schölkopf
(2015). 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

R. Chaves, C. Majenz, L. Luft, TO. Maciel, D. Janzing, B. Schölkopf, D. Gross
(2015). InformationTheoretic Implications of Classical and Quantum Causal Structures 18th Conference on Quantum Information Processing (QIP 2015), State: accepted

J. Peters, JM. Mooij, D. Janzing, B. Schölkopf
(2014). Causal Discovery with Continuous Additive Noise Models Journal of Machine Learning Research, 15, 20092053

R. Chaves, L. Luft, TO. Maciel, D. Gross, D. Janzing, B. Schölkopf
(2014). Inferring latent structures via information inequalities In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, (Ed) NL Zhang and J Tian, AUAI Press, Corvallis, Oregon, 112121, UAI 2014

S. Kpotufe, E. Sgouritsa, D. Janzing, B. Schölkopf
(2014). Consistency of Causal Inference under the Additive Noise Model In: Proceedings of the 31st International Conference on Machine Learning, W&CP 32 (1), (Ed) Eric P. Xing and Tony Jebara, JMLR, 478495, ICML 2014

P. Geiger, D. Janzing, B. Schölkopf
(2014). Estimating Causal Effects by Bounding Confounding In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence , (Ed) Nevin L. Zhang and Jin Tian, AUAI Press Corvallis, Oregon , 240249 , UAI 2014

D. Janzing, D. Balduzzi, M. GrosseWentrup, B. Schölkopf
(2013). Quantifying causal influences Annals of Statistics, 41, (5), 23242358

J. Mooij, D. Janzing, B. Schölkopf
(2013). From Ordinary Differential Equations to Structural Causal Models: the deterministic case In: Proceedings of the TwentyNinth Conference Annual Conference on Uncertainty in Artificial Intelligence, (Ed) A Nicholson and P Smyth, AUAI Press, Corvallis, Oregon, 440448, UAI 2013

E. Sgouritsa, D. Janzing, J. Peters, B. Schölkopf
(2013). Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders In: Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), (Ed) A Nicholson and P Smyth, AUAI Press Corvallis, Oregon, USA, 556565, UAI 2013

J. Peters, D. Janzing, B. Schölkopf
(2013). Causal Inference on Time Series using Restricted Structural Equation Models In: Advances in Neural Information Processing Systems 26, (Ed) C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger, 154162, 27th Annual Conference on Neural Information Processing Systems (NIPS 2013)

B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, J. Mooij
(2013). Semisupervised learning in causal and anticausal settings In: Empirical Inference, (Ed) B Schölkopf, Z Luo, and V Vovk, SpringerVerlag, 129–141

AE. Allahverdyan, KV. Hovhannisyan, D. Janzing, G. Mahler
(2012). Thermodynamic limits of dynamic cooling Physical Review E, 84, (4), 16 pages

D. Janzing, J. Mooij, K. Zhang, J. Lemeire, J. Zscheischler, P. Daniušis, B. Steudel, B. Schölkopf
(2012). Informationgeometric approach to inferring causal directions Artificial Intelligence, 182183, 131

B. Schölkopf, D. Janzing, J. Peters, E. Sgouritsa, K. Zhang, J. Mooij
(2012). On Causal and Anticausal Learning In: Proceedings of the 29th International Conference on Machine Learning (ICML), (Ed) J Langford and J Pineau, Omnipress, New York, NY, USA, 12551262, ICML 2012

D. Janzing, E. Sgouritsa, O. Stegle, J. Peters, B. Schölkopf
(2011). Detecting lowcomplexity unobserved causes (Ed) FG Cozman and A Pfeffer, AUAI Press, Corvallis, OR, USA, 383391, ISBN: 9780974903972, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)

J. Mooij, D. Janzing, B. Schölkopf, T. Heskes
(2011). On Causal Discovery with Cyclic Additive Noise Models In: Advances in Neural Information Processing Systems 24, (Ed) J ShaweTaylor and RS Zemel and PL Bartlett and FCN Pereira and KQ Weinberger, Curran Associates, Inc., Red Hook, NY, USA, 639647, TwentyFifth Annual Conference on Neural Information Processing Systems (NIPS 2011)

J. Peters, J. Mooij, D. Janzing, B. Schölkopf
(2011). Identifiability of causal graphs using functional models (Ed) FG Cozman and A Pfeffer, AUAI Press, Corvallis, OR, USA, 589598, ISBN: 9780974903972, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)

K. Zhang, J. Peters, D. Janzing, B. Schölkopf
(2011). Kernelbased Conditional Independence Test and Application in Causal Discovery (Ed) FG Cozman and A Pfeffer, AUAI Press, Corvallis, OR, USA, 804813, ISBN: 9780974903972, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)

M. Besserve, D. Janzing, NK. Logothetis, B. Schölkopf
(2011). Finding dependencies between frequencies with the kernel crossspectral density IEEE, Piscataway, NJ, USA, 20802083 , ISBN: 9781457705380 , IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)

J. Zscheischler, D. Janzing, K. Zhang
(2011). Testing whether linear equations are causal: A free probability theory approach (Ed) Cozman, F.G. , A. Pfeffer, AUAI Press, Corvallis, OR, USA, 839847, ISBN: 9780974903972, 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011)

J. Peters, D. Janzing, B. Schölkopf
(2011). Causal Inference on Discrete Data using Additive Noise Models IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, (12), 24362450

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

D. Janzing
(2010). On the Entropy Production of Time Series with Unidirectional Linearity Journal of Statistical Physics, 138, (45), 767779

D. Janzing, B. Schölkopf
(2010). Causal Inference Using the Algorithmic Markov Condition IEEE Transactions on Information Theory, 56, (10), 51685194

D. Janzing, B. Steudel
(2010). Justifying Additive Noise ModelBased Causal Discovery via Algorithmic Information Theory Open Systems and Information Dynamics, 17, (2), 189212

I. Guyon, D. Janzing, B. Schölkopf
(2010). Causality: Objectives and Assessment In: JMLR Workshop and Conference Proceedings: Volume 6 , (Ed) I Guyon and D Janzing and B Schölkopf, MIT Press, Cambridge, MA, USA, 142, Causality: Objectives and Assessment (NIPS 2008 Workshop)

B. Steudel, D. Janzing, B. Schölkopf
(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: The 23rd Annual Conference on Learning Theory, OmniPress, Madison, WI, USA, 464476, COLT 2010

D. Janzing, P. Hoyer, B. Schölkopf
(2010). Telling cause from effect based on highdimensional observations In: Proceedings of the 27th International Conference on Machine Learning, (Ed) J Fürnkranz and T Joachims, Proceedings of the 27th International Conference on Machine Learning (ICML 2010), International Machine Learning Society, Madison, WI, USA, 479486, ISBN: 9781605589077, ICML 2010

J. Peters, D. Janzing, A. Gretton, B. Schölkopf
(2010). Kernel Methods for Detecting the Direction of Time Series In: Advances in Data Analysis, Data Handling and Business Intelligence, (Ed) A Fink and B Lausen and W Seidel and A Ultsch, Advances in Data Analysis, Data Handling and Business Intelligence, Springer, Gesellschaft für Klassifikation, Berlin, Germany, 5766, ISBN: 9783642010446, 32nd Annual Conference of the Gesellschaft für Klassifikation e.V. (GfKl 2008)

K. Zhang, B. Schölkopf, D. Janzing
(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, Uncertainty in Artificial Intelligence: Proceedings of the TwentySixth Conference (UAI 2010), AUAI Press, Corvallis, OR, USA, 717724, ISBN: 9780974903965, UAI 2010

JM. Mooij, O. Stegle, D. Janzing, K. Zhang, B. Schölkopf
(2010). Probabilistic latent variable models for distinguishing between cause and effect In: Advances in Neural Information Processing Systems 23, (Ed) J Lafferty and CKI Williams and J ShaweTaylor and RS Zemel and A Culotta, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, Curran, Red Hook, NY, USA, 16871695, ISBN: 9781617823800, 24th Annual Conference on Neural Information Processing Systems (NIPS 2010)

J. Peters, D. Janzing, B. Schölkopf
(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, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010), JMLR, Cambridge, MA, USA, 597604, 13th International Conference on Artificial Intelligence and Statistics

P. Daniusis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang, B. Schölkopf
(2010). Inferring deterministic causal relations In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, (Ed) P Grünwald and P Spirtes, Uncertainty in Artificial Intelligence: Proceedings of the TwentySixth Conference (UAI 2010), AUAI Press, Corvallis, OR, USA, 143150, ISBN: 9780974903965, UAI 2010

AE. Allahverdyan, D. Janzing, G. Mahler
(2009). Thermodynamic efficiency of information and heat flow Journal of Statistical Mechanics: Theory and Experiment, 2009, (P09011), 135

JM. Mooij, D. Janzing, J. Peters, B. Schölkopf
(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 and L Bottou and M Littman, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 745752, ICML 2009

PO. Hoyer, D. Janzing, JM. Mooij, J. Peters, B. Schölkopf
(2009). Nonlinear causal discovery with additive noise models In: Advances in neural information processing systems 21, (Ed) D Koller and D Schuurmans and Y Bengio and L Bottou, Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008, Curran, Red Hook, NY, USA, 689696, ISBN: 9781605609492, 22nd Annual Conference on Neural Information Processing Systems (NIPS 2008)

J. Peters, D. Janzing, A. Gretton, B. Schölkopf
(2009). Detecting the Direction of Causal Time Series In: Proceedings of the 26th International Conference on Machine Learning, (Ed) A Danyluk and L Bottou and ML Littman, Proceedings of the 26th International Conference on Machine Learning (ICML 2009), ACM Press, New York, NY, USA, 801808, ICML 2009

D. Janzing, J. Peters, JM. Mooij, B. Schölkopf
(2009). Identifying confounders using additive noise models In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, (Ed) J Bilmes and AY Ng, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, Corvallis, OR, USA, 249257, ISBN: 9780974903958 , UAI 2009

X. Sun, D. Janzing, B. Schölkopf
(2008). Causal Reasoning by Evaluating the Complexity of Conditional Densities with Kernel Methods Neurocomputing, 71, (79), 12481256

D. Janzing, P. Wocjan, S. Zhang
(2008). A Singleshot Measurement of the Energy of Product States in a Translation Invariant Spin Chain Can Replace Any Quantum Computation New Journal of Physics, 10, (093004), 118

AE. Allahverdyan, D. Janzing
(2008). Relating the Thermodynamic Arrow of Time to the Causal Arrow Journal of Statistical Mechanics, 2008, (P04001), 121

D. Janzing, B. Steudel
(2007). Quantum broadcasting problem in classical lowpower signal processing Physical Review A, 75, (2), 11 pages

X. Sun, D. Janzing
(2007). Learning causality by identifying common effects with kernelbased dependence measures In: ESANN 2007, Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), DSide, Evere, Belgium, 453458, 15th European Symposium on Artificial Neural Networks

X. Sun, D. Janzing
(2007). Exploring the causal order of binary variables via exponential hierarchies of Markov kernels In: ESANN 2007, Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), DSide, Evere, Belgium, 465470, 15th European Symposium on Artificial Neural Networks

X. Sun, D. Janzing, B. Schölkopf, K. Fukumizu
(2007). A KernelBased Causal Learning Algorithm In: Proceedings of the 24th International Conference on Machine Learning(ICML 2007), (Ed) Z Ghahramani, Proceedings of the 24th Annual International Conference on Machine Learning (ICML 2007), ACM Press, New York, NY, USA, 855862, ICML 2007

X. Sun, D. Janzing, B. Schölkopf
(2007). Distinguishing Between Cause and Effect via KernelBased Complexity Measures for Conditional Distributions In: Proceedings of the 15th European Symposium on Artificial Neural Networks , (Ed) M Verleysen, Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN 2007), DSide Publications, Evere, Belgium, 441446, ESANN 2007

X. Sun, D. Janzing, B. Schölkopf
(2006). Causal Inference by Choosing Graphs with Most Plausible Markov Kernels In: Proceedings of the 9th International Symposium on Artificial Intelligence and Mathematics, Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics (AI & Math 2006, 111, ISAIM 2006

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