Elias Bareinboim

来自集智百科 - 复杂系统|人工智能|复杂科学|复杂网络|自组织
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基本信息

Elias Bareinboim
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.

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