**(I joined Carnegie Mellon University as an assistant professor in the philosophy department in 2015, and I am also affiliated to the machine learning department.)**

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.

- First, 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.

- 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."

- fundamental
**characterization**of causal information in observational data, and refinement of concepts related to causality - precise notion of “model
**complexity**” for causal inference - machine learning beyond the
**i.i.d.**setting - unified/universal
**approach**for causal inference **domain-specific**causal inference (in finance, brain signal analysis, etc.)- causal
**understanding**of machine learning tasks - practical causal inference system for
**large-scale**problems - domain
**adaptation** **big data**analytics: a causal perspective- computational
**finance**

- Causal discovery: Theory and applications
- developing advanced and practical computational methods for causal inference
- finding fundamental and testable principles to characterize causality
- latent variable modeling

- Statistical machine learning and applications
- kernel methods, Gaussian processes, domain adaptation, mixture models, model selection, independent component analysis, sparse coding

- Computational finance
- Neuroscience (especially MEG and EEG data analysis)

- Organizational activities
- Co-organizer of the Munich Workshop on Causal Inference and Information Theory (MCI), May 23-24, 2016 (with Negar Kiyavash and Gerhard Kramer)
- Co-organizer of the 2016 ACM SIGKDD Workshop of Causal Discovery (With Jiuyong Li, Elias Bareinboim, and Lin Liu), 2016
- Guest editor of the Journal of Data Science and Analytics Special Issue on Causal Discovery (with Jiuyong Li, Elias Bareinboim, and Lin Liu), 2016
- Organizer of ICML'14 workshop "Causal modeling and machine learning" (with Bernhard Schölkopf, Elias Bareinboim, and Jiji Zhang), June, 2014
- Guest editor of ACM Transactions on Intelligent Systems and Technology special issue on Causality (with Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, and Judea Pearl)
- Organizer of workshop "Causality: Perspectives from different disciplines" (with Bernhard Schölkopf and Jiji Zhang), Vals, Switzerland, August 5-8, 2013
- Co-organizer of the First IEEE ICDM Workshop on Causal Discovery (CD 2013), Dallas, Texas, USA, December 8, 2013
- Co-organizer of workshop “Networks -- Processes and causality”, Menorca, Spain, September, 2012
- Publicity chair of AISTATS 2012 (15th International Conference on Artificial Intelligence and Statistics)

- Reviewer for journals
- 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
- 2017: AISTATS (SPC), IJCAI (SPC), AAAI...
- 2016: AISTATS (SPC), AAAI, KDD, ICML, IJCAI (SPC), NIPS (area chair), UAI (SPC)...
- 2015: AISTATS, KDD, UAI, NIPS, IJCAI, ECML-PKDD, AMBN;
- 2014: AISTATS (SPC), UAI, NIPS, WSDM, KDD (both research & industry tracks),ACML, iKDD CoDS;
- 2013: UAI, NIPS, AISTATS, SDM, KDD, IJCAI, IJCNN, ASE/IEEE Big Data;
- 2012: UAI, AISTATS, MLSP, WSDM, SDM;
- 2011: UAI, NIPS, KDD, IJCNN, ICONIP;
- 2010: UAI, NIPS, ICA/LVA, SDM, ACML, ICPR;
- 2009: NIPS, ACML, ICONIP

58 results
(BibTeX)

** Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows**
*IEEE 17th International Conference on Data Mining (ICDM 2017)*, 2017 (conference) Accepted

**Causal Discovery from Temporally Aggregated Time Series**
*Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence (UAI 2017)*, 2017, ID 269 (conference) Accepted

**Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination**
*Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017)*, 2017 (conference) Accepted

**Model Selection for Gaussian Mixture Models**
*Statistica Sinica*, 27(1):147-169, 2017 (article)

**On the Identifiability and Estimation of Functional Causal Models in the Presence of Outcome-Dependent Selection**
*Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)*, pages: 825-834, (Editors: Ihler, A. and Janzing, D.), AUAI Press, 2016, plenary presentation (conference)

**Learning Causal Interaction Network of Multivariate Hawkes Processes**
*Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016)*, 2016, poster presentation (conference)

**Causal discovery and inference: concepts and recent methodological advances**
*Applied Informatics*, 3(3):1-28, 2016 (article)

**Domain Adaptation with Conditional Transferable Components**
*Proceedings of the 33nd International Conference on Machine Learning (ICML 2016)*, 48, pages: 2839-2848, JMLR Workshop and Conference Proceedings, (Editors: Balcan, M.-F. and Weinberger, K. Q.), 2016 (conference)

**Special Issue on Causal Discovery and Inference**
*ACM Transactions on Intelligent Systems and Technology (TIST)*, 7(2), January 2016, (Guest Editors) (misc)

**On estimation of functional causal models: General results and application to post-nonlinear causal model**
*ACM Transactions on Intelligent Systems and Technologies*, 7(2), January 2016 (article)

**Recent Methodological Advances in Causal Discovery and Inference**
In *15th Conference on Theoretical Aspects of Rationality and Knowledge*, pages: 23-35, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

**Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 1917–1925, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Discovering Temporal Causal Relations from Subsampled Data**
In *Proceedings of the 32nd International Conference on Machine Learning*, 37, pages: 1898–1906, JMLR Workshop and Conference Proceedings, (Editors: F. Bach and D. Blei), JMLR, ICML, 2015 (inproceedings)

**Distinguishing Cause from Effect Based on Exogeneity**
In *Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge*, pages: 261-271, (Editors: Ramanujam, R.), TARK, 2015 (inproceedings)

**Identification of Time-Dependent Causal Model: A Gaussian Process Treatment**
In *24th International Joint Conference on Artificial Intelligence, Machine Learning Track*, pages: 3561-3568, (Editors: Yang, Q. and Wooldridge, M.), AAAI Press, Palo Alto, California USA, IJCAI15, 2015 (inproceedings)

**Multi-Source Domain Adaptation: A Causal View**
In *Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence*, pages: 3150-3157, AAAI Press, AAAI, 2015 (inproceedings)

**Likelihood and Consilience: On Forster’s Counterexamples to the Likelihood Theory of Evidence**
*Philosophy of Science, Supplementary Volume 2015*, 82(5):930-940, 2015 (article)

**A Permutation-Based Kernel Conditional Independence Test**
In *Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI2014)*, pages: 132-141, (Editors: Nevin L. Zhang and Jin Tian), AUAI Press Corvallis, Oregon, UAI2014, 2014 (inproceedings)

**Causal discovery via reproducing kernel Hilbert space embeddings**
*Neural Computation*, 26(7):1484-1517, 2014 (article)

**Single-Source Domain Adaptation with Target and Conditional Shift**
In *Regularization, Optimization, Kernels, and Support Vector Machines*, pages: 427-456, 19, Chapman & Hall/CRC Machine Learning & Pattern Recognition, (Editors: Suykens, J. A. K., Signoretto, M. and Argyriou, A.), Chapman and Hall/CRC, Boca Raton, USA, 2014 (inbook)

**Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method**
In *13th International Conference on Data Mining*, pages: 1003-1008, (Editors: H. Xiong, G. Karypis, B. M. Thuraisingham, D. J. Cook and X. Wu), IEEE Computer Society, ICDM, 2013 (inproceedings)

**Domain adaptation under Target and Conditional Shift**
In *Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3)*, pages: 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 (inproceedings)

**On estimation of functional causal models: Post-nonlinear causal model as an example**
In *First IEEE ICDM workshop on causal discovery *, 2013, Held in conjunction with the 12th IEEE International Conference on Data Mining (ICDM 2013) (inproceedings)

**Semi-supervised learning in causal and anticausal settings**
In *Empirical Inference*, pages: 129-141, 13, Festschrift in Honor of Vladimir Vapnik, (Editors: Schölkopf, B., Luo, Z. and Vovk, V.), Springer, 2013 (inbook)

**On Causal and Anticausal Learning**
In *Proceedings of the 29th International Conference on Machine Learning*, pages: 1255-1262, (Editors: J Langford and J Pineau), Omnipress, New York, NY, USA, ICML, 2012 (inproceedings)

**Information-geometric approach to inferring causal directions**
*Artificial Intelligence*, 182-183, pages: 1-31, May 2012 (article)

**Causal discovery with scale-mixture model for spatiotemporal variance dependencies
**
In *Advances in Neural Information Processing Systems 25*, pages: 1736-1744, (Editors: P Bartlett and FCN Pereira and CJC. Burges and L Bottou and KQ Weinberger), Curran Associates Inc., 26th Annual Conference on Neural Information Processing Systems (NIPS), 2012 (inproceedings)

**A general linear non-Gaussian state-space model: Identifiability, identification, and applications**
In *JMLR Workshop and Conference Proceedings Volume 20*, pages: 113-128, (Editors: Hsu, C.-N. , W.S. Lee ), MIT Press, Cambridge, MA, USA, 3rd Asian Conference on Machine Learning (ACML), November 2011 (inproceedings)

**Testing whether linear equations are causal: A free probability theory approach**
In pages: 839-847, (Editors: Cozman, F.G. , A. Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**Kernel-based Conditional Independence Test and Application in Causal Discovery**
In pages: 804-813, (Editors: FG Cozman and A Pfeffer), AUAI Press, Corvallis, OR, USA, 27th Conference on Uncertainty in Artificial Intelligence (UAI), July 2011 (inproceedings)

**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)

**Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity**
*Journal of Machine Learning Research*, 11, pages: 1709-1731, May 2010 (article)

**Inferring deterministic causal relations**
In *Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence*, pages: 143-150, (Editors: P Grünwald and P Spirtes), AUAI Press, Corvallis, OR, USA, UAI, July 2010 (inproceedings)

**Source Separation and Higher-Order Causal Analysis of MEG and EEG**
In *Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Sixth Conference (UAI 2010)*, pages: 709-716, (Editors: Grünwald, P. , P. Spirtes), AUAI Press, Corvallis, OR, USA, 26th Conference on Uncertainty in Artificial Intelligence (UAI), July 2010 (inproceedings)

**Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models**
In *JMLR Workshop and Conference Proceedings, Volume 6*, pages: 157-164, (Editors: I Guyon and D Janzing and B Schölkopf), MIT Press, Cambridge, MA, USA, Causality: Objectives and Assessment (NIPS Workshop), 2010 (inproceedings)

**Convolutive blind source separation by efficient blind deconvolution and minimal filter distortion**
*Neurocomputing*, 73(13-15):2580-2588, August 2010 (article)

**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)

**Multi-Label Learning by Exploiting Label Dependency**
In *Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010)*, pages: 999-1008, (Editors: Rao, B. , B. Krishnapuram, A. Tomkins, Q. Yang), ACM Press, New York, NY, USA, 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), July 2010 (inproceedings)

**Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective **
In *Machine Learning and Knowledge Discovery in Databases*, pages: 570-585, (Editors: Buntine, W. , M. Grobelnik, D. Mladenić, J. Shawe-Taylor ), Springer, Berlin, Germany, European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (ECML PKDD '09), September 2009 (inproceedings)

**ICA with Sparse Connections: Revisited **
In *Independent Component Analysis and Signal Separation*, pages: 195-202, (Editors: Adali, T. , Christian Jutten, J.M. Travassos Romano, A. Kardec Barros), Springer, Berlin, Germany, 8th International Conference on Independent Component Analysis and Signal Separation (ICA), March 2009 (inproceedings)

**On the Identifiability of the Post-Nonlinear Causal Model**
In *Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (UAI 2009)*, pages: 647-655, (Editors: Bilmes, J. , A. Y. Ng, D. A. McAllester), AUAI Press, Corvallis, OR, USA, 25th Conference on Uncertainty in Artificial Intelligence (UAI), June 2009 (inproceedings)

**Efficient factor GARCH models and factor-DCC models**
*Quantitative Finance*, 9(1):71-91, 2009 (article)

**Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis**
*Journal of Machine Learning Research*, 9, pages: 2455-2487, 2008 (article)

**Independent Factor Reinforcement Learning for Portfolio Management**
In *Proceedings of the 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2007)*, pages: 1020-1031, (Editors: H Yin and P Tiño and E Corchado and W Byrne and X Yao), Springer, Berlin, Germany, 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), 2007 (inproceedings)

**Separating convolutive mixtures by pairwise mutual information minimization", IEEE Signal Processing Letters**
*IEEE Signal Processing Letters*, 14(12):992-995, 2007 (article)

**Kernel-Based Nonlinear Independent Component Analysis**
In *Independent Component Analysis and Signal Separation, 7th International Conference, ICA 2007*, pages: 301-308, (Editors: M E Davies and C J James and S A Abdallah and M D Plumbley), Springer, 7th International Conference on Independent Component Analysis and Signal Separation (ICA), 2007, Lecture Notes in Computer Science, Vol. 4666 (inproceedings)

**Nonlinear independent component analysis with minimum nonlinear distortion**
In *ICML ’07: Proceedings of the 24th international conference on Machine learning*, pages: 1127-1134, (Editors: Z Ghahramani), ACM, New York, NY, USA, 24th International Conference on Machine Learning (ICML), June 2007 (inproceedings)

**Dimension Reduction as a Deflation Method in ICA**
*IEEE Signal Processing Letters*, 13(1):45-48, 2006 (article)

**Extensions of ICA for Causality Discovery in the Hong Kong Stock Market**
In *Neural Information Processing, 13th International Conference, ICONIP 2006*, pages: 400-409, (Editors: I King and J Wang and L Chan and D L Wang), Springer, 13th International Conference on Neural Information Processing (ICONIP), 2006, Lecture Notes in Computer Science, 2006, Volume 4234/2006 (inproceedings)

**Enhancement of source independence for blind source separation**
In *Independent Component Analysis and Blind Signal Separation, LNCS 3889*, pages: 731-738, (Editors: J. Rosca and D. Erdogmus and JC Príncipe und S. Haykin), Springer, Berlin, Germany, 6th International Conference on Independent Component Analysis and Blind Signal Separation (ICA), 2006, Lecture Notes in Computer Science, 2006, Volume 3889/2006 (inproceedings)