Assistant Professor
Department of Computer Science Department of Statistics (by courtesy) Purdue University Email: eb at purdue dot edu Twitter: @eliasbareinboim Address: 305 N. University Street 2142L West Lafayette, IN, 47907-2107. |

[ summary – news – teaching – service – tutorials – talks – publications ]

I obtained my Ph.D. from the Computer Science Department at UCLA, advised by Judea Pearl.
My research focuses on causal inference and its applications to bioinformatics, economics, medicine, and public health. Specifically, my research is concerned with the problem of generalizing causal and statistical knowledge from heterogeneous datasets, including due to issues of external validity, meta-analysis, and selection bias.
A recent summary of this work in the context of combining massive sets of research data just appeared at PNAS, see story and paper.

I am broadly interested in Artificial Intelligence, Machine Learning, Statistics, Cognitive Science, and Philosophy of Science.

My CV: pdf (Nov/20, 2017)

- I am joining the Editorial Board of the Journal of Causal Inference (link), consider submitting your work.
- I am co-organizing the 7th UAI Causality Workshop: Learning, Inference, and Decision-Making (link), consider submitting your work.
- Our work on solving big data's fusion problem and combining massive sets of research data just appeared at PNAS, see story and paper.
- I am honored to be selected by IEEE Intelligent Systems as one of AI's 10 To Watch (story, pdf).
- I am co-organizing the 2016 ACM SIGKDD Workshop on Causal Discovery (link) and the 2016 UAI Workshop on Causation: Foundation to Application (link), consider submitting your work.
- Our paper "Recovering from selection bias in causal and statistical inference" was selected as a notable paper in computing in 2014, to appear in the ACM Computing Reviews' 19th Annual Best of Computing (see full list here).
- I will join the Computer Science Department at Purdue as an Assistant Professor in the Fall/2015.
- I was selected as the 2014 Edward K. Rice Outstanding Doctoral Student. This award is given to a single PhD student in all engineering and applied sciences majors at UCLA.
- Our paper "Recovering from Selection Bias in Causal and Statistical Inference" (link) just received the best paper award (1 out 1406 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-14).
- I am honored that I was selected as the "Outstanding Graduating PhD Student" (commencement award), Computer Science, UCLA.
- I received the "Google Outstanding Graduate Research Award", Computer Science, UCLA.
- I am honored to be selected as one of the 2014 Dan David Scholars for "outstanding achievement and future promise" in the field of Artificial Intelligence (citation here).
- I am co-organizing an ICML-14 workshop on Causal Modeling & Machine Learning (with B. Schölkopf, K. Zhang, J. Zhang), consider submitting your work, link.
- I am a guest editor (with J. Pearl, B. Schölkopf, K. Zhang, J. Li) of ACM Transactions on Intelligent Systems and Technology on "Causal Discovery and Inference". See the call for papers.
- With Judea Pearl, I gave a tutorial on "Causes and Counterfactuals: Concepts, Principles and Tools" at NIPS 2013. The video (with slides) is available online, link (requires HTML5).
- The video of my talk on meta-transportability in AISTATS-2013 is now available here.

- Spring/2018: CS 47100 (ugrad), Introduction to Artificial Intelligence [syllabus / link]
- Fall/2017: CS 59000-AML / STAT 59800 (grad), Advanced Machine Learning (Causal Inference) [syllabus / link]
- Spring/2017: CS 47100 (ugrad), Introduction to Artificial Intelligence [syllabus / link]
- Fall/2016: CS 59000-AI (grad), Artificial Intelligence [syllabus / link]
- Spring/2016: CS 59000-AML / STAT 59800 (grad), Advanced Machine Learning (Causal Inference) [syllabus / link]
- Fall/2015: CS 57800 / STAT 59000 (grad), Machine Learning [syllabus / link]

- Conferences (program committee): AAAI-17, NIPS-17, UAI-17, AISTATS-17, IJCAI-16, AAAI-16, NIPS-16, UAI-16, ECAI-16, NIPS-15, UAI-15, AAAI-15, AISTATS-15, KDD-DI-14, UAI-14, AISTATS-14, ICML-14, Causal-NIPS-13, IEEE-BigData-13, IJCAI-13, AAAI-13, UAI-13, ICML-13, UAI-12, ICML-12, IJCAI-11, NIPS-11(Rev), UAI-11, MMIS-ICDM-11, KR-10(Rev).

- Journals (reviewer): ACM Computing Surveys, Annals of Applied Statistics, Scandinavian Journal of Statistics, The British Journal for the Philosophy of Science, Statistics in Medicine, Epidemiology, Biometrics, Statistics, J. of Machine Learning Research (JMLR), J. of Causal Inference, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Bioinformatics, J. of Proteome Research, J. of Proteomics, Physica A.

- "Causal Inference and the Data-Fusion Problem", International Conference on Autonomous Agents and Multi-agent Systems (AAMAS), Sao Paulo, May/2017.
- "Introduction to Causal Inference", West Coast Experiments Conference, Los Angeles, CA, Apr/2017.
- "Causal Inference and the Data-Fusion Problem", Association for Advancement of Artificial Intelligence (AAAI), San Francisco, CA, Feb/2017.
- "Causal Inference and the Data-Fusion Problem", Department of Computing Science, University of Alberta, Edmonton, Canada, Aug/2016.
- (with J. Pearl) "Causes and Counterfactuals: Concepts, principles, and tools", NIPS, Lake Tahoe, NV, Dec/2013.
- "Causality and Big Data", EMC2 Summer School on Big Data, Rio de Janeiro, Brazil, Feb/2013.
- "An Introduction to Causal Inference", 2nd IEEE Conf. on Healthcare Informatics, Imaging, and Systems Biology, La Jolla, CA, Sep/2012.

- CVPR-17 Workshop "Functionality, Physics, Intentionality and Causality (FPIC)", Honolulu, HI, Jul/2017.
- Statistical Society of Canada, Annual Meeting, Winnipeg, Canada, Jun/2017.
- School of Engineering, University of São Paulo (USP), São Paulo, Brazil, May/2017.
- Institute of Computing, University of Campinas (UNICAMP), Campinas, Brazil, May/2017.
- Advancing the Science of Transportation Demand Modeling (NSF), UC Berkeley, CA, Apr/2017.
- West Coast Experiments Conference (Sloan), UCLA, CA, Apr/2017.
- University of Wisconsin, Madison, Mar/2017.
- University of Southern California (USC), Los Angeles, CA, Feb/2017.
- NIPS-16 Workshop "Inference and Learning of Hypothetical and Counterfactual Interventions in Complex Systems", Barcelona, Spain, Dec/2016.
- AAAI-16 Fall Symposium on Accelerating Science: A Grand Challenge for AI (plenary talk), Arlington, VA, Nov/2016.
- Department of Public Health Sciences, University of Chicago, Chicago, Nov/2016.
- 54th Allerton Conference on Communication, Control, and Computing, UIUC, IL, Sep/2016.
- Department of Computing Science, University of Alberta, Edmonton, Canada, Aug/2016.
- International Conference on Thinking (ICT), Providence, RI, Aug/2016.
- Joint Statistical Meetings (JSM), Chicago, IL, Jul/2016.
- CRM Causal Inference Workshop, Montreal, Canada, Jul/2016
- Max Planck Institute, Tübingen, Germany, May/2016.
- Department of Computer Science and Mathematics, University of Passau, Germany, May/2016.
- Munich Workshop on Causal Inference and Information Theory (MCI), Munich, Germany, May/2016.
- Statistics Colloquium, Purdue University, West Lafaytte, IN, Feb/2016.
- Purdue University, West Lafayette IN, Apr/2015.
- Johns Hopkins University, Baltimore, MD, Mar/2015.
- University of California - Berkeley, Berkeley, CA, Mar/2015.
- University of Southern California (USC), Los Angeles, CA, Mar/2015.
- University of California - Irvine, Irvine, CA, Mar/2015.
- Cornell University, Ithaca, NY, Feb/2015.
- Stanford University, Stanford, CA, Feb/2015.
- University of Chicago, Chicago, IL, Dec/2014.
- Kyoto International Conference on Modern Statistics, Kyoto, Japan, Nov/2014.
- International Workshop on Causal Inference and its Related Topics, Tokyo, Japan, Nov/2014.
- SIGKDD-14 Workshop on Discovery Informatics, New York, Aug/2014.
- UAI-14 Workshop on Causality: Learning and Prediction, Quebec City, Canada, July/2014.
- Institute of Mathematical Statistics (IMS) Annual Meeting, Sydney, Australia, Jul/2014.
- NICTA, Sydney, Australia, Jul/2014.
- 2014 Atlantic Causal Inference Conference, Providence, RI, May/2014.
- Joint Mathematics Meetings (Big Data: Math and Stats Modeling), American Mathematical Society (AMS), Baltimore, MD, Jan/2014.
- (with J. Pearl) NIPS-13 Workshop on Causality (Large-scale Experimental Design and Inference of Causal Mechanisms), Lake Tahoe, NV, Dec/2013.
- (with J. Pearl) MURI/ONR, UCLA, Los Angeles, California, Oct/2012, Sept/2013, Dec/2014.
- Graduate School of Engineering, COPPE/Federal University of Rio de Janeiro (UFRJ), Brazil, May/2012.
- Computer Science Colloquium, CS-Math/Federal University of Rio de Janeiro (UFRJ), Brazil, May/2012.
- International Workshop on Mining Multiple Information Sources, International Conference on Data Mining (ICDM), Vancouver, Canada, Dec/2011.
- 58th World Congress of Statistics, International Statistics Institute (ISI), Dublin, Ireland, Aug/2011.
- DERI/National University of Ireland (NUI), Galway, Ireland, Aug/2011.

**
Fairness in Decision-Making -- The Causal Explanation Formula **

J. Zhang, E. Bareinboim.

AAAI-18. In *Proceedings of the 32nd AAAI Conference on Artificial Intelligence,* 2018, forthcoming.

*Purdue AI Lab, Technical Report (R-30)*, Nov, 2017. [pdf]

**
Generalized Adjustment Under Confounding and Selection Biases **

J. Correa, J. Tian, E. Bareinboim.

AAAI-18. In *Proceedings of the 32nd AAAI Conference on Artificial Intelligence,* 2018, forthcoming.

*Purdue AI Lab, Technical Report (R-29)*, Nov, 2017. [pdf]

**
Experimental Design for Learning Causal Graphs with Latent Variables **

M. Kocaoglu, K. Shanmugam, E. Bareinboim.

NIPS-17. In *Proceedings of the 31st Annual Conference on Neural Information Processing Systems*, 2017, forthcoming.

*Purdue AI Lab, Technical Report (R-28)*, Nov, 2017. [pdf]

**
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables **

B. Chen, D. Kumor, E. Bareinboim.

ICML-17. In *Proceedings of the 34th International Conference on Machine Learning*, 2017.

*Purdue AI Lab, Technical Report (R-27)*, Jun, 2017. [pdf]

**
Counterfactual Data-Fusion for Online Reinforcement Learners
**

A. Forney, J. Pearl, E. Bareinboim.

ICML-17. In *Proceedings of the 34th International Conference on Machine Learning*, 2017.

*Purdue AI Lab, Technical Report (R-26)*, Jun, 2017. [pdf]

**
Transfer Learning in Multi-Armed Bandits: A Causal Approach **

J. Zhang, E. Bareinboim.

IJCAI-17. In *Proceedings of the 26th International Joint Conference on Artificial Intelligence*, 2017.

*Purdue AI Lab, Technical Report (R-25)*, Jun, 2017. [pdf]

**
Causal Effect Identification by Adjustment under Confounding and Selection Biases **

J. Correa, E. Bareinboim.

AAAI-17. In *Proceedings of the 31st AAAI Conference on Artificial Intelligence*, 2017.

*Purdue AI Lab, Technical Report (R-24)*, Nov, 2016.
[pdf]

**Causal inference and the data-fusion problem**

E. Bareinboim, J. Pearl.

PNAS-16. *Proceedings of the National Academy of Sciences*, v. 113 (27), pp. 7345-7352, 2016. [pdf]

**
Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models **

B. Chen, J. Pearl, E. Bareinboim.

IJCAI-16. In *Proceedings of the 25th International Joint Conference on Artificial Intelligence*, 2016.
[pdf]

**Comment on "Causal Inference using invariance prediction: identification and confidence intervals (by Peters, Buhlmann and Meinshausen)"**

E. Bareinboim.

RSS-16. *Journal of the Royal Statistical Society*, Series B, forthcoming.

**
Markov Decision Processes with Unobserved Confounders: A Causal Approach **

J. Zhang, E. Bareinboim.

*Purdue AI Lab, Technical Report (R-23)*, 2016.
[pdf]

**Bandits with Unobserved Confounders: A Causal Approach**

E. Bareinboim, A. Forney, J. Pearl.

NIPS-15. In *Proceedings of the 28th Annual Conference on Neural Information Processing Systems*, 2015.
[pdf]

**Recovering Causal Effects From Selection Bias**

E. Bareinboim, J. Tian.

AAAI-15. In *Proceedings of the 29th AAAI Conference on Artificial Intelligence*, 2015.
[pdf]

**Transportability from Multiple Environments with Limited Experiments: Completeness Results**

E. Bareinboim, J. Pearl.

NIPS-14. In *Proceedings of the 27th Annual Conference on Neural Information Processing Systems*, 2014.
[pdf]

**Spotlight Presentation (62 out of 1678 papers).**

**Recovering from Selection Bias in Causal and Statistical Inference**

E. Bareinboim, J. Tian, J. Pearl.

AAAI-14. In *Proceedings of the 28th AAAI Conference on Artificial Intelligence*, 2014.
[pdf]

Supplemental material, UCLA Cognitive Systems Laboratory, Technical Report (R-425-sup).
[pdf]

**Best Paper Award (1 out of 1406 papers).**

**
External Validity: From do-calculus to Transportability across Populations
**

J. Pearl, E. Bareinboim.

StSci-14. *Statistical Science*, v. 29(4), pp. 579-595, 2014.
[pdf]

**Generalizability in Causal Inference: Theory and Algorithms**

E. Bareinboim.

Ph.D. Thesis, Computer Science Department, UCLA, 2014.

**Causal Transportability from Multiple Environments with Limited Experiments**

E. Bareinboim, S. Lee, V. Honavar, J. Pearl.

NIPS-13. In *Proceedings of the 26th Annual Conference on Neural Information Processing Systems*, 2013.
[pdf]

**Causal Transportability with Limited Experiments**

E. Bareinboim, J. Pearl.

AAAI-13. In *Proceedings of the 27th AAAI Conference on Artificial Intelligence*, 2013.
[pdf]

**Meta-Transportability of Causal Effects: A formal approach**

E. Bareinboim, J. Pearl.

AISTATS-13. In *Proceedings of the 16th International Conference on Artificial Intelligence and Statistics*, 2013.
[pdf]

** A General Algorithm for Deciding Transportability of Experimental Results**

E. Bareinboim, J. Pearl.

JCI-13. *Journal of Causal Inference*, v. 1(1), pp. 107--134, 2013.
[pdf]

** Causal Inference by Surrogate Experiments**

E. Bareinboim, J. Pearl.

UAI-12. In *Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence*, 2012.
[pdf]

**Transportability of Causal Effects: Completeness Results**

E. Bareinboim, J. Pearl.

AAAI-12. In *Proceedings of the 26th AAAI Conference on Artificial Intelligence*, 2012.
[pdf]

**Controlling Selection Bias in Causal Inference**

E. Bareinboim, J. Pearl.

AISTATS-12. In *Proceedings of the 15th International Conference on Artificial Intelligence and Statistics*, 2012.
[pdf]

**Local Characterizations of Causal Bayesian Networks**

E. Bareinboim, C. Brito, J. Pearl.

LNAI-12. In * Lecture Notes in Artificial Intelligence, Springer*, 2012.
[pdf]

**Transportability of Causal and Statistical relations: A formal approach**

J. Pearl, E. Bareinboim.

AAAI-11. In *Proceedings of the 25th AAAI Conference on Artificial Intelligence*, 2011.
[pdf]

Extended Technical Report (R-372), UCLA Cognitive Systems Laboratory.
[pdf]

**Controlling Selection Bias in Causal Inference** (Short paper)

E. Bareinboim, J. Pearl.

AAAI-11. In *Proceedings of the 25th AAAI Conference on Artificial Intelligence*, 2011.
[pdf]

**External Validity and Transportability: A Formal Approach**

J. Pearl, E. Bareinboim.

JSM-ASA-11. In *Proceedings of the Joint Statistical Meetings, American Statistical Association*, 2011.
[pdf]

**Local Characterizations of Causal Bayesian Networks**

E. Bareinboim, C. Brito, J. Pearl.

GKR-IJCAI-11. In *Proceedings of the GKR-22nd International Joint Conference on Artificial Intelligence*, 2011.
[pdf]

**Analyzing marginal cases in differential shotgun proteomics**

P. Carvalho, J. Fischer, J. Perales, J. Yates III, V. Barbosa, E. Bareinboim.

*Bioinformatics*, Vol 27, pp. 275-276, 2011.
[pdf]

**Descents and nodal load in scale-free networks**

E. Bareinboim, V.C. Barbosa.

*Physical Review E*, Vol. 77, 046111, 2008.
[pdf]

January 10, 2018.