There has been a long history of debate on causality in philosophy, statistics, economics, and related fields. I have been concerned with this classic question—how can we discover causal information from purely observed data (i.e., perform causal inference)? How such causal information can facilitate solving other problems such as modeling, prediction, and control, is also interesting to me.
My research consists of three main lines.
 Firstly, I have focused on developing practical computational methods for causal inference, to produce more reliable causal information.
 Secondly, to better understand causality and derive more universal methods for causal inference, I also work on finding fundamental and testable principles that help discover causality from data.
 Thirdly, latent variable modeling is closely related to causality, and it has been interesting me for over eight years. Developing more general yet identifiable latent variable models would benefit the causality field, as well as the machine learning and signal processing communities.
Since machine learning plays a key role in data analysis as well as causal inference, I am also very interested in this field.
*What's new*
 The workshop "Causal modeling & machine learning" will take place in Beijing, China, in June 2014.
 We are editing the ACM Transactions on Intelligent Systems and Technologies (ACM TIST) special issue on causal discovery and inference; see the call for papers here. Submission deadline: 14 March 2014.
 The workshop "Causality: Perspectives from different disciplines" took place in August, 2013.
 Slides and poster for a recent paper "Domain adaptation under target and conditional shift."
Ongoing projects:
 fundamental characterization of causal information in observational data, and refinement of concepts related to causality
 precise notion of “model complexity” for causal inference
 unified/universal approach for causal inference
 domainspecific causal inference (in finance, brain signal analysis, etc.)
 causal understanding of machine learning tasks
 practical causal inference system for largescale problems
 domain adaptation
 big data analytics: a causal perspective
 computational finance
Research Interests
· Causal discovery: Theory and applications
o developing advanced and practical computational methods for causal inference
o finding fundamental and testable principles to characterize causality
o latent variable modeling
· Statistical machine learning and applications
o kernel methods, Gaussian processes, domain adaptation, mixture models, model selection, independent component analysis, sparse coding
· Computational finance
· Neuroscience (especially MEG and EEG data analysis)
Academic Service
· Organizational activities
o Organizer of ICML'14 workshop "Causal modeling and machine learning" (with Bernhard Schölkopf, Elias Bareinboim, and Jiji Zhang), June, 2014
o Guest editor of ACM Transactions on Intelligent Systems and Technologie special issue on Causality (with Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, and Judea Pearl)
o Organizer of workshop "Causality: Perspectives from different disciplines" (with Bernhard Schölkopf and Jiji Zhang), Vals, Switzerland, August 58, 2013
o Coorganizer of the First IEEE ICDM Workshop on Causal Discovery (CD 2013), Dallas, Texas, USA, December 8, 2013
o Coorganizer of workshop “Networks  Processes and causality”, Menorca, Spain, September, 2012
o Publicity chair of AISTATS 2012 (15th International Conference on Artificial Intelligence and Statistics)
· Reviewer for journals
o Annals of Statistics; Journal of Machine Learning Research; Annals of Applied Statistics; Journal of the American Statistical Association; Neural Computation; Machine Learning; IEEE Transactions on Pattern Analysis and Machine Intelligence; IEEE Transactions on Neural Networks; IEEE Transactions on Signal Processing; Neural Networks; IEEE Transactions on Knowledge and Data Engineering; Quantitative Finance; Neurocomputing; IEEE Signal Processing Letters; Frontiers of Computer Science; International Journal of Imaging Systems and Technology; Circuits, Systems & Signal Processing; International Review of Economics and Finance
· Program committee member for international conferences
o 2014: AISTATS (senior program committee), UAI, NIPS, WSDM, KDD, iKDD CoDS...
o 2013: UAI, NIPS, AISTATS, SDM, KDD, IJCAI, IJCNN, ASE/IEEE Big Data;
o 2012: UAI, AISTATS, MLSP, WSDM, SDM;
o 2011: UAI, NIPS, KDD, IJCNN, ICONIP;
o 2010: UAI, NIPS, ICA/LVA, SDM, ACML, ICPR;
o 2009: NIPS, ACML, ICONIP

B. Huang, K. Zhang, B. Schölkopf
(2015). Identification of TimeDependent Causal Model: A Gaussian Process Treatment In: 24th International Joint Conference on Artificial Intelligence, Machine Learning Track, IJCAI15, State: submitted

G. Doran, K. Muandet, K. Zhang, B. Schölkopf
(2014). A PermutationBased Kernel Conditional Independence Test In: Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014), (Ed) Nevin L. Zhang and Jin Tian, AUAI Press Corvallis, Oregon, 132–141, UAI2014

K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Z. Zhou, C. Persello
(2014). SingleSource Domain Adaptation with Target and Conditional Shift In: Regularization, Optimization, Kernels, and Support Vector Machines, (Ed) JAK Suykens, M Signoretto, and A Argyriou, Chapman and Hall/CRC, Boca Raton, USA, 427456

K. Zhang, B. Schölkopf, K. Muandet, Z. Wang
(2013). Domain adaptation under Target and Conditional Shift In: Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), (Ed) S Dasgupta and D McAllester, JMLR, 819–827, ICML 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

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

Z. Chen, K. Zhang, L. Chan
(2012). Causal discovery with scalemixture model for spatiotemporal variance dependencies In: Advances in Neural Information Processing Systems 25, (Ed) P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger, Curran Associates Inc., 1736–1744, 26th Annual Conference on Neural Information Processing Systems (NIPS 2012)

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

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)

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)

K. Zhang, A. Hyvärinen
(2011). A general linear nonGaussian statespace model: Identifiability, identification, and applications In: JMLR Workshop and Conference Proceedings Volume 20, (Ed) Hsu, C.N. , W.S. Lee , MIT Press, Cambridge, MA, USA, 113128, 3rd Asian Conference on Machine Learning (ACML 2011)

K. Zhang, A. Hyvärinen
(2010). Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models In: JMLR Workshop and Conference Proceedings, Volume 6, (Ed) I Guyon and D Janzing and B Schölkopf, MIT Press, Cambridge, MA, USA, 157164, Causality: Objectives and Assessment (NIPS 2008 Workshop)

K. Zhang, A. Hyvärinen
(2010). Source Separation and HigherOrder Causal Analysis of MEG and EEG (Ed) Grünwald, P. , P. Spirtes, Uncertainty in Artificial Intelligence: Proceedings of the TwentySixth Conference (UAI 2010), AUAI Press, Corvallis, OR, USA, 709716, ISBN: 9780974903965, 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

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

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

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)

ML. Zhang, K. Zhang
(2010). MultiLabel Learning by Exploiting Label Dependency (Ed) Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang, Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), ACM Press, New York, NY, USA, 9991008, 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)

K. Zhang, H. Peng, L. Chan, A. Hyvärinen
(2009). ICA with Sparse Connections: Revisited In: Independent Component Analysis and Signal Separation, (Ed) Adali, T. , Christian Jutten, J.M. Travassos Romano, A. Kardec Barros, Springer, Berlin, Germany, 195202, ISBN: 9783642005992, 8th International Conference on Independent Component Analysis and Signal Separation (ICA 2009)

K. Zhang, A. Hyvärinen
(2009). Causality Discovery with Additive Disturbances: An InformationTheoretical Perspective In: Machine Learning and Knowledge Discovery in Databases, (Ed) Buntine, W. , M. Grobelnik, D. Mladenić, J. ShaweTaylor , Springer, Berlin, Germany, 570585, ISBN: 9783642041747, European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (ECML PKDD ’09)

K. Zhang, A. Hyvärinen
(2009). On the Identifiability of the PostNonlinear Causal Model (Ed) Bilmes, J. , A. Y. Ng, D. A. McAllester, Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009), AUAI Press, Corvallis, OR, USA, 647655, ISBN: 9780974903958 , 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)

K. Zhang, L. Chan
(2007). KernelBased Nonlinear Independent Component Analysis In: Independent Component Analysis and Signal Separation, 7th International Conference, ICA 2007, (Ed) M E Davies and C J James and S A Abdallah and M D Plumbley, Springer, 301308, 7th International Conference on Independent Component Analysis and Signal Separation (ICA 2007)

J. Li, K. Zhang, L. Chan
(2007). Independent Factor Reinforcement Learning for Portfolio Management In: Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007), (Ed) H Yin and P Tiño and E Corchado and W Byrne and X Yao, Springer, Berlin, Germany, 10201031, 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007)

K. Zhang, L. Chan
(2007). Nonlinear independent component analysis with minimum nonlinear distortion In: ICML ’07: Proceedings of the 24th international conference on Machine learning, (Ed) Z Ghahramani, ACM, New York, NY, USA, 11271134, 24th International Conference on Machine Learning (ICML 2007)

K. Zhang, L. Chan
(2006). Enhancement of source independence for blind source separation In: Independent Component Analysis and Blind Signal Separation, LNCS 3889, (Ed) J. Rosca and D. Erdogmus and JC Príncipe und S. Haykin, Springer, Berlin, Germany, 731738, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006)

K. Zhang, L. Chan
(2006). ICA by PCA Approach: Relating HigherOrder Statistics to SecondOrder Moments In: Independent Component Analysis and Blind Signal Separation, (Ed) J P Rosca and D Erdogmus and J C Príncipe and S Haykin, Springer, 311318, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA 2006)

K. Zhang, L. Chan
(2006). Extensions of ICA for Causality Discovery in the Hong Kong Stock Market In: Neural Information Processing, 13th International Conference, ICONIP 2006, (Ed) I King and J Wang and L Chan and D L Wang, Springer, 400409, 13th International Conference on Neural Information Processing (ICONIP 2006)

K. Zhang, L. Chan
(2006). ICA with Sparse Connections In: Intelligent Data Engineering and Automated Learning – IDEAL 2006, (Ed) E Corchado and H Yin and V Botti und Colin Fyfe, Springer, 530537, 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2006)

K. Zhang, L. Chan
(2005). To apply score function difference based ICA algorithms to highdimensional data In: Proceedings of the 13th European Symposium on Artificial Neural Networks (ESANN 2005), 291297, 13th European Symposium on Artificial Neural Networks (ESANN 2005)

K. Zhang, L. Chan
(2003). Dimension Reduction Based on Orthogonality — a Decorrelation Method in ICA In: Artificial Neural Networks and Neural Information Processing  ICANN/ICONIP 2003, (Ed) O Kaynak and E Alpaydin and E Oja and L Xu, Springer, Berlin, Germany, 132139, International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003