Elias Bareinboim

Director, Causal Artificial Intelligence Lab CausalAI Lab
Associate Professor, Department of Computer Science
Columbia University

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Contact information:
Twitter: @eliasbareinboim
Email: eb at cs dot columbia dot edu

500 W 120th St (Mudd bldg)
New York, NY, 10027

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Summary

Summary

I am the director of the Causal Artificial Intelligence Lab and an associate professor in the Department of Computer Science at Columbia University. Prior to joining Columbia, I was an assistant professor at Purdue University. Before that, I obtained my Ph.D. in Computer Science at the University of California, Los Angeles, advised by Judea Pearl. I am broadly interested in Artificial Intelligence, Machine Learning, Statistics, Robotics, Cognitive Science, and Philosophy of Science.

My research focuses on causal inference and its applications to data-driven fields (i.e., data science) in the health and social sciences as well as artificial intelligence and machine learning. I am particularly interested in understanding how to make robust and generalizable causal and counterfactual claims in the context of heterogeneous and biased data collections, including due to issues of confounding bias, selection bias, and external validity (transportability). A survey of recent developments on this topic, when combining massive sets of research data, appeared at the Proceedings of the National Academy of Sciences (PNAS), see the story and the paper. A brief summary of the automated scientist project was also highlighted at the IEEE Intelligent Systems (link, story). For an overview of my thoughts on causal data science, as of April 2019, watch the talk I recently gave at Columbia University, link.

More recently, I have been exploring the intersection of causal inference with decision-making/reinforcement learning (NeurIPS-15, ICML-17, IJCAI-17, NeurIPS-18, AAAI-19) and explainability/fairness analysis (AAAI-18, NeurIPS-18).

Additional information (Jul/10, 2019) -- CV (pdf), short bio (txt), hi-res picture (jpg).

News

Teaching

Academic Service

Tutorials

Invited talks



Publications

2019:

General Identifiability with Arbitrary Surrogate Experiments
S. Lee, J. Correa, E. Bareinboim.
UAI-19. In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-46), Apr, 2019. [pdf]

From Statistical Transportability to Estimating the Effects of Stochastic Interventions
J. Correa, E. Bareinboim.
IJCAI-19. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-45), May, 2019, forthcoming.

On Causal Identification under Markov Equivalence
A. Jaber, JJ. Zhang, E. Bareinboim
IJCAI-19. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-44), May, 2019, forthcoming.

Adjustment Criteria for Generalizing Experimental Findings
J. Correa, J. Tian, E. Bareinboim.
ICML-19. In Proceedings of the 36th International Conference on Machine Learning, 2019.
Columbia CausalAI Lab, Technical Report (R-43), Apr, 2019. [pdf]

Causal Identification under Markov Equivalence: Completeness Results
A. Jaber, JJ. Zhang, E. Bareinboim
ICML-19. In Proceedings of the 36th International Conference on Machine Learning, 2019.
Columbia CausalAI Lab, Technical Report (R-42), Apr, 2019. [pdf]

Sensitivity Analysis of Linear Structural Causal Models
C. Cinelli, D. Kumor, B. Chen, J. Pearl, E. Bareinboim
ICML-19. In Proceedings of the 36th International Conference on Machine Learning, 2019.
Columbia CausalAI Lab, Technical Report (R-41), Apr, 2019. [pdf]

Structural Causal Bandits with Non-manipulable Variables
S. Lee, E. Bareinboim
AAAI-19. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-40), Nov, 2018. [pdf]

Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding
A. Forney, E. Bareinboim.
AAAI-19. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-39), Nov, 2018. [pdf]

Identification of Causal Effects in the Presence of Selection Bias
J. Correa, J. Tian, E. Bareinboim.
AAAI-19. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, 2019.
Columbia CausalAI Lab, Technical Report (R-38), Nov, 2018. [pdf]

2018:

Equality of Opportunity in Classification: A Causal Approach
J. Zhang, E. Bareinboim
NeurIPS-18. In Proceedings of the 32nd Annual Conference on Neural Information Processing Systems, 2018.
Columbia CausalAI Lab, Technical Report (R-37), 2018. [pdf]

Structural Causal Bandits: Where to Intervene?
S. Lee, E. Bareinboim
NeurIPS-18. In Proceedings of the 32nd Annual Conference on Neural Information Processing Systems, 2018.
Columbia CausalAI Lab, Technical Report (R-36), September, 2018. [pdf, code]

Causal Identification under Markov Equivalence
A. Jaber, JJ. Zhang, E. Bareinboim
UAI-18. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, 2018.
Columbia CausalAI Lab, Technical Report (R-35), August, 2018. [pdf]
Best Student Paper Award (1 out of 337 papers).

Non-Parametric Path Analysis in Structural Causal Models
J. Zhang, E. Bareinboim
UAI-18. In Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, 2018.
Columbia CausalAI Lab, Technical Report (R-34), May, 2018. [pdf]

Budgeted Experimental Design for Causal Structural Learning
A. Ghassami, S. Salehkaleybar, N. Kiyavash, E. Bareinboim
ICML-18. In Proceedings of the 35th International Conference on Machine Learning, 2018.
Columbia CausalAI Lab, Technical Report (R-33), May, 2018. [pdf]

A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams
A. Jaber, JJ. Zhang, E. Bareinboim.
IJCAI-18. In Proceedings of the 26th International Joint Conference on Artificial Intelligence, 2018.
Columbia CausalAI Lab, Technical Report (R-32), May, 2018. [pdf]

A note on "Generalizability of Study Results (Lesko et al., 2017)"
J. Pearl, E. Bareinboim.
EPI-18. Epidemiology, 2018, forthcoming.
Columbia CausalAI Lab, Technical Report (R-31), Apr, 2018. [pdf]

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.
Columbia CausalAI 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.
Columbia CausalAI Lab, Technical Report (R-29), Nov, 2017. [pdf]
Outstanding Paper Award Honorable Mention (2 out of 3800 papers).

2017:

Experimental Design for Learning Causal Graphs with Latent Variables
M. Kocaoglu, K. Shanmugam, E. Bareinboim.
NeurIPS-17. In Proceedings of the 31st Annual Conference on Neural Information Processing Systems, 2017.
Columbia CausalAI 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.
Columbia CausalAI 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.
Columbia CausalAI 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.
Columbia CausalAI 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.
Columbia CausalAI 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.
Columbia CausalAI Lab, Technical Report (R-21), 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.
Columbia CausalAI Lab, Technical Report (R-22), 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.
Columbia CausalAI Lab, Technical Report (R-20), 2016.

Markov Decision Processes with Unobserved Confounders: A Causal Approach
J. Zhang, E. Bareinboim.
Columbia CausalAI Lab, Technical Report (R-23), 2016. [pdf]

2015:

Bandits with Unobserved Confounders: A Causal Approach
E. Bareinboim, A. Forney, J. Pearl.
NeurIPS-15. In Proceedings of the 28th Annual Conference on Neural Information Processing Systems, 2015.
Columbia CausalAI Lab, Technical Report (R-19), 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.
Columbia CausalAI Lab, Technical Report (R-18), 2015. [pdf]

2014:

Transportability from Multiple Environments with Limited Experiments: Completeness Results
E. Bareinboim, J. Pearl.
NeurIPS-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.
NeurIPS-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 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]

Pre-PhD:

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


July 10, 2019.