Elias Bareinboim

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/
联系邮箱	[[1]]
个人cv	chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://causalai.net/cv-bareinboim.pdf

Bareinboim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。

教育经历

• Ph.D. in Computer Science – University of California, Los Angeles (UCLA), 2014. Title: Generalizability in Causal Inference: Theory and Algorithms. Advisor: Judea Pearl.

计算机科学与技术博士学位 2014年于加利福尼亚大学洛杉矶分校课题：因果推理中的泛化性：理论与算法导师：朱迪亚·珀尔

• B.Sc., M.Sc. in Computer Science – Federal University of Rio de Janeiro (UFRJ), 2007. Title: Descents and nodal load in scale-free networks. Advisor: Valmir C. Barbosa.

计算机科学与技术学士学位硕士学位 2017年于里约热内卢联邦大学课题：无标度网络中的下降和节点负载导师：瓦勒米尔·巴尔沃萨

学术职位

Associate Professor, Computer Science, Columbia University, Summer/2019-now.

哥伦比亚大学计算机科学与技术系副教授 2019夏-至今

• Director, Causal Artificial Intelligence Laboratory. • Member, Data Science Institute.

因果AI实验室主任

数学科学研究所成员

• Member, Program for Mathematical Genomics (since 2022).

基因组学数学项目组成员

• Assistant Professor, Computer Science, Purdue University, Fall/2015-Spring/2019.

普渡大学计算机科学与技术系助理教授 2015年秋-2019年春

• Director, Causal Artificial Intelligence Laboratory.

因果AI实验室主任

Assistant Professor, courtesy appointment, Statistics.

礼任统计系助理教授

• Faculty Affiliate, Regenstrief Center for Healthcare Engineering.

医疗保健工程中心兼职教授

Postdoctoral Scholar, Cognitive Systems Lab/UCLA, Judea Pearl, Fall/2014-Summer/2015.

加州大学洛杉矶分校认知系统实验室博士后研究员，导师：朱迪亚·珀尔，2014年秋-2015年夏

Research Assistant, Cognitive Systems Lab/UCLA, Judea Pearl, Fall/2009-Summer/2014.

加州大学洛杉矶分校认知系统实验室研究助理，导师：朱迪亚·珀尔，2009年秋-2014年夏

获奖经历

Bareinboim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。

• 2022 ONR Young Investigator Award.

2022年 ONR青年科学家奖

• 2021 JP Morgan Faculty Research Award (gift, $120,000).

2021年 JP摩根教授研究奖（奖励，120000美元）

• 2020 Amazon Research Award (gift, $90,000).

2020年亚马逊研究奖（奖励，90000美元）

• 2019 UAI Best Paper Award (1 out of 450 papers).

2019年 UAI 最佳论文奖（450篇论文中的1篇）

• 2018 NSF Faculty Early Career Development (CAREER) Award. • 2018 Adobe Data Science Research Award (gift, $50,000).

2018年 NSF 教员早期生涯奖

2018年 Adobe 数据科学研究奖（奖励，50000美元）

• 2018 UAI Best Student Paper Award (1 out of 337 papers).

2018年 UAI 最佳论文奖（337篇论文中的1篇）

• 2018 AAAI Outstanding Paper Award Honorable Mention (2 out of 3800 papers).

2018年 AAAI 杰出论文奖优秀奖（3800篇论文中的2篇）

• 2017 IBM Open Collaborative Award (gift, $50,000).

2017年 IBM 开放协作奖（奖励，50000美元）

• 2016 IEEE AI’s 10 to Watch, Intelligent Systems.

2016年 IEEE AI的十大看点之智能系统

• 2015 ACM Notable Paper, 19th Annual Best of Computing, Computing Reviews.

2015年 ACM杰出论文，第19届计算机技术年度最佳评论

• 2014 UCLA Edward K. Rice Outstanding Doctoral Student Award (given to a single PhD student in all engineering and applied sciences majors), School of Engineering and Applied Sciences, UCLA.

2014年 UCLA爱德华赖斯杰出博士生奖（在所有的工程和应用科学专业仅及于一个博士生）

• 2014 AAAI Outstanding Paper Award (1 out of 1406 papers).

2014年 AAAI 杰出论文奖优秀奖（1406篇论文中的1篇）

• 2014 UCLA Outstanding Graduating PhD Student (commencement award), Computer Science.

2014年 UCLA杰出博士生奖（毕业典礼奖）计算机科学与技术

• 2014 Google Outstanding Graduate Research Award, Computer Science, UCLA.

2014年 UCLA 谷歌杰出毕业生研究奖计算机科学与技术

• 2014 Dan David Scholar, Future Dimension: Artificial Intelligence ($15,000), Dan David Foundation.

2014年丹大卫学者奖，未来视角：人工智能（奖金，15000美元），丹大卫基金会

• 2013 UCLA Dissertation Year Fellowship (DYF) (~$35,000).

2013年 UCLA博士论文年度奖学金(奖金，35000美元)

• 2012 Yahoo! Key Scientific Challenges Award, area Machine Learning & Statistics ($5,000).

2012年雅虎机器学习与统计领域重大科学挑战奖

• 2008 UCLA Ph.D.’s Fellowship (~$45,000).

2008年 UCLA博士奖学金

• 2008 Top 10 award – National contest of M.Sc. thesis (2007), Brazilian Computer Society.

2007年巴西计算机学会全国理学硕士论文竞赛前十名

• 2008-2012 Ph.D.’s Fellowship, Fulbright – U.S. Dep. of State / CAPES-MEC, declined.

2008年-2012年福布莱特博士奖学金

• 2003-2007 Undergraduate’s and Master’s Fellowships, Brazilian Research Council CNPq.

2003年-2007年巴西研究理事会本科生和硕士研究生奖学金

主要文章及著作

Bareinboim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，为组合在不同实验条件下生成并受到各种偏差困扰的数据集提供了实用的方法。最近，Bareinboim 一直在探索因果推理与决策（包括强化学习）和可解释性（包括公平性分析）的交叉点。 Bareinboim 获得了博士学位。来自加州大学洛杉矶分校，在那里他得到了 Judea Pearl 的建议。

2022

Neural Causal Models for Counterfactual Identification and Estimation Columbia CausalAI Laboratory, Technical Report

Kevin Xia, Yushu Pan, Elias Bareinboim (2022) May/2022.

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness Columbia CausalAI Laboratory, Technical Report

Amin Jaber, Adele Ribeiro, Jiji Zhang, Elias Bareinboim (2022) May/2022.

Causal Imitation Learning via Inverse Reinforcement Learning Columbia CausalAI Laboratory, Technical Report

Darren Kangrui, Junzhe Zhang, Sharon Di, Elias Bareinboim (2022) May/2022.

Online Reinforcement Learning for Mixed Policy Scopes Columbia CausalAI Laboratory, Technical Report

Junzhe Zhang, Elias Bareinboim (2022) May/2022.

Scores for Learning Discrete Causal Graphs with Unobserved Confounders Columbia CausalAI Laboratory, Technical Report

Alexis Bellot, Junzhe Zhang, Elias Bareinboim (2022) May/2022.

Counterfactual Transportability: A Formal Approach Columbia CausalAI Laboratory, Technical Report

Juan Correa, Sanghack Lee, Elias Bareinboim (2022) May/2022. Proceedings of the 38th International Conference on Machine Learning (ICML), in press. (Acceptance rate = 21%)

On Measuring Causal Contributions via do-Interventions Columbia CausalAI Laboratory, Technical Report

Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Elias Bareinboim (2022) May/2022. Proceedings of the 38th International Conference on Machine Learning (ICML), in press. (Acceptance rate = 21%)

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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图灵奖得主朱迪亚·珀尔教授认为，当下正在进行一场改变数据科学的新革命 ”因果革命“。它以科学为中心，涉及从数据到政策、可解释性、机制的泛化，再到一些社会科学中的归因和公平性问题，甚至哲学中的创造性和自由意志。本季读书会以Elements of Causal Inference一书为线索，主要展现因果科学在机器学习各个方向上的影响，包括强化学习、迁移学习、表示学习等等，并分享在工业界的部分应用成果。本季读书会梳理了因果科学的核心内容，理解它如何改变数据科学，助力 AI 系统超越曲线拟合和获得回答因果问题的能力。

因果科学与Causal AI读书会第二季

因果推断与机器学习领域的结合已经吸引了越来越多来自学界业界的关注。第一季读书会主要关注了因果科学在机器学习方向上的前沿应用，为深入探讨、普及推广因果科学议题，第二季读书会着力于实操性、基础性，带领大家精读因果科学方向两本非常受广泛认可的入门教材：Causal inference in statistics: A primer和Elements of causal inference: foundations and learning algorithms。读书会以直播讨论为主，结合习题交流、夜谈、编程实践、前沿讲座等多类型内容，主要面向有机器学习背景、希望深入学习因果科学基础知识和重要模型方法、寻求解决相关研究问题的研究人员。

因果科学读书会第三季：因果+X

“因果”并不是一个新概念，而是一个已经在多个学科中使用了数十年的分析技术。通过前两季的分享，我们主要梳理了因果科学在计算机领域的前沿进展。如要融会贯通，我们需要回顾数十年来在社会学、经济学、医学、生物学等多个领域中，都是使用了什么样的因果模型、以什么样的范式、解决了什么样的问题。我们还要尝试进行对比和创新，看能否以现在的眼光，用其他的模型，为这些研究提供新的解决思路。

“因果+X”就是要让因果真正地应用于我们的科学研究中，不管你是来自计算机、数理统计领域，还是社会学、经济学、管理学领域，还是医学、生物学领域，我们希望共同探究出因果研究的范式，真正解决因果的多学科应用问题，乃至解决工业界的问题。