Elias Bareinboim

      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.





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Summary

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 (June/10, 2017)

News

Teaching

Academic Service

Tutorials

Invited talks



Publications

2017:

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, forthcoming.
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, forthcoming.
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, forthcoming.

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]

2016:

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]

2015:

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]

2014:

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.

2013:

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]

2012:

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]

2011:

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

Pre-PhD:

Descents and nodal load in scale-free networks
E. Bareinboim, V.C. Barbosa.
Physical Review E, Vol. 77, 046111, 2008. [pdf]

Grammatical inference applied to linguistic modeling of biological regulation networks
E. Bareinboim, A. Vasconselos, J. Silva
Eletronic Journal of Communication Information & Innovation in Health, Vol 1, S. pp. 329–333, 2007.

Characterizing Regulatory Regions in E.coli using Augmented Regular Expressions
G. Menezes, E. Bareinboim, J. Silva, A. Vasconselos
In Proceedings of the 3rd Annual Brazilian Association for Bioinformatics and Computational Biology (AB3C) Conference, 2007.


June 12, 2017.