**(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

62 results
(View BibTeX file of all listed publications)

**Generalized Score Functions for Causal Discovery**
*Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)*, pages: 1551-1560, (Editors: Yike Guo and Faisal Farooq), ACM, August 2018 (conference)

**Learning Causality and Causality-Related Learning: Some Recent Progress**
*National Science Review*, 5(1):26-29, 2018 (article)

** Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows**
*IEEE 17th International Conference on Data Mining (ICDM)*, pages: 913-918, (Editors: Vijay Raghavan,Srinivas Aluru, George Karypis, Lucio Miele and Xindong Wu), November 2017 (conference)

**Causal Discovery from Temporally Aggregated Time Series**
*Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017*, pages: ID 269, (Editors: Gal Elidan, Kristian Kersting, and Alexander T. Ihler), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017 (conference)

**Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination**
*Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI)*, pages: 1347-1353, (Editors: Carles Sierra), August 2017 (conference)

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

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

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

**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)*, pages: 825-834, (Editors: Ihler, A. and Janzing, D.), AUAI Press, June 2016 (conference)

**On estimation of functional causal models: General results and application to post-nonlinear causal model**
*ACM Transactions on Intelligent Systems and Technologies*, 7(2):article no. 13, January 2016 (article)

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

**Nonlinear functional causal models for distinguishing cause from effect**
In *Statistics and Causality: Methods for Applied Empirical Research*, pages: 185-201, 8, 1st, (Editors: Wolfgang Wiedermann and Alexander von Eye), John Wiley & Sons, Inc., 2016 (inbook)

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

**Preface to the ACM TIST Special Issue on Causal Discovery and Inference**
*ACM Transactions on Intelligent Systems and Technologies*, 7(2):article no. 17, 2016 (article)

**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)

**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)

**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)

**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)

**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)

**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)

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

**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)

**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)

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

**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)

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

**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)

**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)

**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)

**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)

**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)

**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)

**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)

**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)