Elias Bareinboim
基本信息
| 类别 | 信息 |
|---|---|
| 姓名 | 埃利亚斯·巴伦博伊姆 Elias Bareinboim |
| 国籍 | 美国 |
| 学术任职 | 哥伦比亚大学因果人工智能实验室主任 |
| 母校 | 学士学位、硕士学位 - 里约热内卢联邦大学 Federal University of Rio de Janeiro
博士学位 - 加利福尼亚大学洛杉矶分校 University of California, Los Angeles |
| 博士导师 | 朱迪亚·珀尔 Judea Pearl |
| 主要研究方向 | 因果性与反事实推断，及其在健康与社会科学领域的应用 |
| 获奖经历 | 2016年IEEE智能系统“人工智能十大新星” |
| 个人主页 | https://causalai.net/ |
埃利亚斯·巴伦博伊姆是哥伦比亚大学计算机系副教授、因果人工智能实验室主任。他曾在加利福尼亚大学洛杉矶分校获得计算机科学博士学位，导师为朱迪亚·珀尔教授。他的研究兴趣包括人工智能、机器学习、统计学、机器人科学、认知科学和科学哲学。他的研究主要关注因果推断和其在健康与社会科学、人工智能、机器学习方向的应用。他的研究特别关注如何在异构和有偏的数据中得出稳健、可泛化的因果和反事实结论，这其中包括了混淆偏差、选择偏差和可迁移性问题。他的工作为数据融合问题提出了第一个通用解决方案，可以组合在不同实验条件、不同偏差下生成的数据集。
成长经历
##还需找其他资料补充一下
埃利亚斯·巴伦博伊姆于2016年被 IEEE 评为“人工智能十大新星”之一，并获得 NSF CAREER Award、ONR Young Investigator Award、Dan David Prize Scholarship、AAAI 2014优秀论文奖和UAI 2019最佳论文奖。
主要文章及著作
##（描述所著文章及书籍的要旨与意义，选取一下这个人的经典文献介绍一下也可以）
2021:
Partial Counterfactual Identification from Observational and Experimental Data
J. Zhang, J. Tian, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-78), Jun, 2021. [pdf, bib]
Effect Identification in Causal Diagrams with Clustered Variables
T. Anand, A. Ribeiro, J. Tian, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-77), Jun, 2021. [pdf, bib]
The Causal-Neural Connection: Expressiveness, Learnability, and Inference
K. Xia, K. Lee, Y. Bengio, E. Bareinboim.
NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-80), Jun, 2021. [pdf, bib]
Nested Counterfactual Identification from Arbitrary Surrogate Experiments
J. Correa, S. Lee, E. Bareinboim.
NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-79), Jun, 2021. [pdf, bib]
Sequential Causal Imitation Learning with Unobserved Confounders
D. Kumor, J. Zhang, E. Bareinboim.
NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-76), Jun, 2021. [pdf, bib]
Oral Presentation (<1%, out of 9122 papers).
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments
Y. Jung, J. Tian, E. Bareinboim.
NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-75), Jun, 2021. [pdf, bib]
Spotlight Presentation (<3%, out of 9122 papers).
Causal Identification with Matrix Equations
S. Lee, E. Bareinboim.
NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-70), Jun, 2021. [pdf, bib]
Oral Presentation (<1%, out of 9122 papers).
Causal Inference and Data Fusion: Towards an Accelerated Process of Scientific Discovery
A. Ribeiro, E. Bareinboim.
OECD-22. Organisation for Economic Co-operation and Development, Volume “AI and the productivity of science”, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-73), Apr, 2022. [pdf, bib]
Non-Parametric Methods for Partial Identification of Causal Effects
J. Zhang, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-72), Feb, 2021. [pdf, bib]
Estimating Identifiable Causal Effects on Markov Equiv. Class through Double Machine Learning
Y. Jung, J. Tian, E. Bareinboim.
ICML-21. In Proceedings of the 38th International Conference on Machine Learning, 2020.
Columbia CausalAI Laboratory, Technical Report (R-71), Feb, 2021. [pdf, bib]
Estimating Identifiable Causal Effects through Double Machine Learning
Y. Jung, J. Tian, E. Bareinboim.
AAAI-21. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021.
Columbia CausalAI Laboratory, Technical Report (R-69), Dec, 2020. [pdf, bib]
Bounding Causal Effects on Continuous Outcomes
J. Zhang, E. Bareinboim.
AAAI-21. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021.
Columbia CausalAI Laboratory, Technical Report (R-61), Jun, 2020. [pdf, bib]
2020:
General Transportability of Soft Interventions: Completeness Results
J. Correa, E. Bareinboim.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems.
Columbia CausalAI Laboratory, Technical Report (R-68), Jun, 2020. [pdf, bib]
Causal Discovery from Soft Interventions with Unknown Targets: Characterization & Learning
A. Jaber, M. Kocaoglu, K. Shanmugam, E. Bareinboim.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems.
Columbia CausalAI Laboratory, Technical Report (R-67), Jun, 2020. [pdf, bib]
Causal Imitation Learning with Unobserved Confounders
J. Zhang, D. Kumor, E. Bareinboim.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems.
Columbia CausalAI Laboratory, Technical Report (R-66), Jun, 2020. [pdf, bib]
Oral Presentation (105 out of 9454 papers).
Can Humans Be Out of the Loop?
J. Zhang, E. Bareinboim.
CleaR-22. In Proceedings of the 1st Conference on Causal Learning and Reasoning, forthcoming.
Columbia CausalAI Laboratory, Technical Report (R-64), Jun, 2020. [pdf, bib]
Characterizing Optimal Mixed Policies: Where to Intervene, What to Observe
S. Lee, E. Bareinboim.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems.
Columbia CausalAI Laboratory, Technical Report (R-63), Jun, 2020. [pdf, bib]
Learning Causal Effects via Weighted Empirical Risk Minimization
Y. Jung, J. Tian, E. Bareinboim.
NeurIPS-20. In Proceedings of the 34th Annual Conference on Neural Information Processing Systems.
Columbia CausalAI Laboratory, Technical Report (R-62), Jun, 2020. [pdf, bib]
On Pearl’s Hierarchy and the Foundations of Causal Inference
E. Bareinboim, J. Correa, D. Ibeling, T. Icard.
ACM-20. In Probabilistic and Causal Inference: The Works of Judea Pearl (ACM, Special Turing Series), pp. 507-556, 2022.
Columbia CausalAI Laboratory, Technical Report (R-60), Jul, 2020. [pdf, bib]
Causal Effect Identifiability under Partial-Observability
S. Lee, E. Bareinboim.
ICML-20. In Proceedings of the 37th International Conference on Machine Learning, 2020.
Columbia CausalAI Laboratory, Technical Report (R-58), Jun, 2020. [pdf, bib]
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach
J. Zhang, E. Bareinboim.
ICML-20. In Proceedings of the 37th International Conference on Machine Learning, 2020.
Columbia CausalAI Laboratory, Technical Report (R-57), Jun, 2020. [pdf, bib]
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets
D. Kumor, C. Cinelli, E. Bareinboim.
ICML-20. In Proceedings of the 37th International Conference on Machine Learning, 2020.
Columbia CausalAI Laboratory, Technical Report (R-56), Jun, 2020. [pdf, bib]
A Calculus For Stochastic Interventions: Causal Effect Identification and Surrogate Experiments
J. Correa, E. Bareinboim.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
Columbia CausalAI Laboratory, Technical Report (R-55), Nov, 2019. [pdf, bib]
Estimating Causal Effects Using Weighting-Based Estimators
Y. Jung, J. Tian, E. Bareinboim.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
Columbia CausalAI Laboratory, Technical Report (R-54), Nov, 2019. [pdf, bib]
Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains
S. Lee, J. Correa, E. Bareinboim.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
Columbia CausalAI Laboratory, Technical Report (R-53), Nov, 2019. [pdf, bib]
Identifiability from a Combination of Observations and Experiments
S. Lee, J. Correa, E. Bareinboim.
AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020.
Columbia CausalAI Laboratory, Technical Report (R-52), Nov, 2019. [pdf, bib]
2019:
Causal Inference and Data-Fusion in Econometrics
P. Hünermund, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-51), Dec, 2019. [pdf, bib]
Identification of Conditional Causal Effects under Markov Equivalence
A. Jaber, J. Zhang, E. Bareinboim.
NeurIPS-19. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems, 2019.
Spotlight Presentation (164 out of 6743 papers).
Columbia CausalAI Laboratory, Technical Report (R-50), Sep, 2019. [pdf, bib]
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets
D. Kumor, B. Chen, E. Bareinboim.
NeurIPS-19. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems, 2019.
Columbia CausalAI Laboratory, Technical Report (R-49), Oct, 2019. [pdf, bib]
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes
J. Zhang, E. Bareinboim.
NeurIPS-19. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems, 2019.
Columbia CausalAI Laboratory, Technical Report (R-48), Oct, 2019. [pdf, bib]
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions
M. Kocaoglu, A. Jaber, K. Shanmugam, E. Bareinboim.
NeurIPS-19. In Proceedings of the 33rd Annual Conference on Neural Information Processing Systems, 2019.
Columbia CausalAI Laboratory, Technical Report (R-47), Oct, 2019. [pdf, bib]
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 Laboratory, Technical Report (R-46), May, 2019. [pdf, bib]
Best Paper Award (1 out of 450 papers).
From Statistical Transportability to Estimating the Effect of Stochastic Interventions
J. Correa, E. Bareinboim.
IJCAI-19. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019.
Columbia CausalAI Laboratory, Technical Report (R-45), May, 2019. [pdf, bib]
On Causal Identification under Markov Equivalence
A. Jaber, JJ. Zhang, E. Bareinboim.
IJCAI-19. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019.
Columbia CausalAI Laboratory, Technical Report (R-44), May, 2019. [pdf, bib]
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 Laboratory, Technical Report (R-43), Apr, 2019. [pdf, bib]
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 Laboratory, Technical Report (R-42), Apr, 2019. [pdf, bib]
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 Laboratory, Technical Report (R-41), Apr, 2019. [pdf, bib]
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 Laboratory, Technical Report (R-40), Nov, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-39), Nov, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-38), Nov, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-37), Oct, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-36), Sep, 2018. [pdf, bib, 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 Laboratory, Technical Report (R-35), Aug, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-34), May, 2018. [pdf, bib]
Budgeted Experiment Design for Causal Structure Learning
A. Ghassami, S. Salehkaleybar, N. Kiyavash, E. Bareinboim.
ICML-18. In Proceedings of the 35th International Conference on Machine Learning, 2018.
Columbia CausalAI Laboratory, Technical Report (R-33), May, 2018. [pdf, bib]
A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams
A. Jaber, JJ. Zhang, E. Bareinboim.
IJCAI-18. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018.
Columbia CausalAI Laboratory, Technical Report (R-32), May, 2018. [pdf, bib]
A note on "Generalizability of Study Results (Lesko et al., 2017)"
J. Pearl, E. Bareinboim.
EPI-18. Epidemiology, v. 30(2), pp. 186-188, 2019.
Columbia CausalAI Laboratory, Technical Report (R-31), Apr, 2018. [pdf, bib]
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 Laboratory, Technical Report (R-30), Nov, 2017. [pdf, bib]
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 Laboratory, Technical Report (R-29), Nov, 2017. [pdf, bib]
Outstanding Paper Award Honorable Mention (2 out of 3800 papers).
近期报道
##将第一人称换为“埃利亚斯·巴伦博伊姆”，超链接可附上
- I am co-organizing with J. Pearl, Y. Bengio, B. Scholkopf, T. Sejnowski the NeurIPS-21 Workshop "Causal Inference & Machine Learning: Why now?" (WHY-21), consider submitting your work (link).
- Our chapter "On Pearl’s Hierarchy and the Foundations of Causal Inference" (with Juan Correa, Duligur Ibeling, Thomas Icard) will appear at an ACM special volume in honor of Judea Pearl and is now available online (link).
- The slides and videos of my tutorial at ICML-20 on the intersection of causal inference and reinforcement learning, which I have been calling "causal reinforcement learning" (CRL), are now available online (link).
- The video of my talk at Columbia University on "causal data science" -- the intersection of causal inference and data science -- is now available online (link).
- Our paper "General Identifiability with Arbitrary Surrogate Experiments" (with Sanghack Lee and Juan Correa, pdf) was selected as the Best Paper Award (1 out 450 papers) at the Uncertainty in Artificial Intelligence conference (UAI-19).
- I am joining the Computer Science Department at Columbia University.
- I am co-organizing with J. Pearl, B. Scholkopf, C. Szepesvari, S. Mahadevan, P. Tadepalli the AAAI-19 Spring Symposium "Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI" (WHY-19), consider submitting your work (link).
- I am thankful for Adobe's generous gift ($50k) and support to our research.
- Our paper "Causal Identification under Markov Equivalence" (with Amin Jaber and Jiji Zhang, link) was selected as the Best Student Paper Award (1 out 337 papers) at the Uncertainty in Artificial Intelligence conference (UAI-18).
- Our paper "Generalized Adjustment Under Confounding and Selection Biases" (with Juan Correa and Jin Tian, link) just received the Outstanding Paper Award Honorable Mention (2 out 3800 papers) at the Annual Conference of the American Association for Artificial Intelligence (AAAI-18).
- I am thankful for IBM's generous gift ($50k) and support to our research and collaboration.
- 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 the Proceedings of the National Academy of Sciences (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. Scholkopf, K. Zhang, JJ. Zhang), consider submitting your work, link.
- I am a guest editor (with J. Pearl, B. Scholkopf, 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 NeurIPS 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.
相关链接
##（包含个人wiki、采访、谷歌学术个人主页等）