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。
获奖经历
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
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。
2021
Partial Counterfactual Identification from Observational and Experimental Data
J. Zhang, J. Tian, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-78), Jun, 2021.
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.
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.
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.
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.
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.
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.
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.
Non-Parametric Methods for Partial Identification of Causal Effects
J. Zhang, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-72), Feb, 2021.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
2019
Causal Inference and Data-Fusion in Econometrics
P. Hünermund, E. Bareinboim.
Columbia CausalAI Laboratory, Technical Report (R-51), Dec, 2019.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Outstanding Paper Award Honorable Mention (2 out of 3800 papers).
近期报道
- 与 J. Pearl, Y. Bengio, B. Scholkopf, T. Sejnowski 共同组织 NeurIPS-21 的 Workshop "Causal Inference & Machine Learning: Why now?" (WHY-21) 链接.
- 与 Juan Correa, Duligur Ibeling, Thomas Icard 共同完成 ACM special volume in honor of Judea Pearl 一书中的 On Pearl’s Hierarchy and the Foundations of Causal Inference 章节 链接.
- 在 ICML-20 发布tutorial Causal Reinforcement Learning，关注因果推断和强化学习的交叉领域 链接.
- 在哥伦比亚大学发表演讲 Causal Data Science，关注因果推断和数据科学的交叉领域 链接.
- 与 Sanghack Lee and Juan Correa 共同完成的论文 General Identifiability with Arbitrary Surrogate Experiments 被评为 UAI-19 会议的最佳论文奖 (1/450).
- 与 J. Pearl, B. Scholkopf, C. Szepesvari, S. Mahadevan, P. Tadepalli 共同组织 AAAI-19 春季研讨会 Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI (WHY-19) 链接.
- 与 Amin Jaber and Jiji Zhang 共同完成的论文 Causal Identification under Markov Equivalence 被评为 UAI-18 会议的最佳学术论文奖 (1/337).
- 与 Juan Correa and Jin Tian 共同完成的论文 Generalized Adjustment Under Confounding and Selection Biases 被评为 AAAI-18 会议的优秀论文奖荣誉提名 (2/3800).
- 成为 Journal of Causal Inference 期刊编委会成员 链接.
- 协办第7届 UAI Causality Workshop: Learning, Inference, and Decision-Making 链接.
- 共同主办 2016 ACM SIGKDD Workshop on Causal Discovery 链接.
- 共同主办 2016 UAI Workshop on Causation: Foundation to Application 链接.