Elias Bareinboim

According to our database, Elias Bareinboim authored at least 102 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning.

Drago Plecko, Elias Bareinboim

Found. Trends Mach. Learn., 2024

Counterfactual Image Editing.

Yushu Pan, Elias Bareinboim

CoRR, 2024

Reconciling Predictive and Statistical Parity: A Causal Approach.

Drago Plecko, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Towards Safe Policy Learning under Partial Identifiability: A Causal Approach.

Shalmali Joshi, Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Transportable Representations for Domain Generalization.

Kasra Jalaldoust, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Scores for Learning Discrete Causal Graphs with Unobserved Confounders.

Alexis Bellot, Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Neural Causal Abstractions.

Kevin Xia, Elias Bareinboim

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Editorial Special Issue on Causality: Fundamental Limits and Applications.

Negar Kiyavash, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak

IEEE J. Sel. Areas Inf. Theory, 2023

Causal Fairness for Outcome Control.

Drago Plecko, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Causal Framework for Decomposing Spurious Variations.

Drago Plecko, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Causal discovery from observational and interventional data across multiple environments.

Adam Li, Amin Jaber, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Nonparametric Identifiability of Causal Representations from Unknown Interventions.

Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments.

Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating Joint Treatment Effects by Combining Multiple Experiments.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2023

Neural Causal Models for Counterfactual Identification and Estimation.

Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Causal Imitation Learning via Inverse Reinforcement Learning.

Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Causal Effect Identification in Cluster DAGs.

Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Causal Fairness Analysis.

Drago Plecko, Elias Bareinboim

CoRR, 2022

Effect Identification in Cluster Causal Diagrams.

Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim

CoRR, 2022

Online Reinforcement Learning for Mixed Policy Scopes.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Finding and Listing Front-door Adjustment Sets.

Hyunchai Jeong, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness.

Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Partial Counterfactual Identification from Observational and Experimental Data.

Junzhe Zhang, Jin Tian, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

On Measuring Causal Contributions via do-interventions.

Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

Counterfactual Transportability: A Formal Approach.

Juan D. Correa, Sanghack Lee, Elias Bareinboim

Proceedings of the International Conference on Machine Learning, 2022

Causal Transportability for Visual Recognition.

Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

Can Humans Be out of the Loop?

Junzhe Zhang, Elias Bareinboim

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

External Validity: From <i>Do</i>-Calculus to Transportability Across Populations.

Judea Pearl, Elias Bareinboim

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

On Pearl's Hierarchy and the Foundations of Causal Inference.

Elias Bareinboim, Juan D. Correa, Duligur Ibeling, Thomas Icard

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

Recovering from Selection Bias in Causal and Statistical Inference.

Elias Bareinboim, Jin Tian, Judea Pearl

Proceedings of the Probabilistic and Causal Inference: The Works of Judea Pearl, 2022

2021

The Causal-Neural Connection: Expressiveness, Learnability, and Inference.

Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Causal Identification with Matrix Equations.

Sanghack Lee, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Sequential Causal Imitation Learning with Unobserved Confounders.

Daniel Kumor, Junzhe Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Nested Counterfactual Identification from Arbitrary Surrogate Experiments.

Juan D. Correa, Sanghack Lee, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The KDD 2021 Workshop on Causal Discovery (CD2021).

Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafillou, Elias Bareinboim, Huan Liu, Negar Kiyavash

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery.

Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash

Proceedings of the KDD 2021 Workshop on Causal Discovery, 2021

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the 38th International Conference on Machine Learning, 2021

Bounding Causal Effects on Continuous Outcome.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Estimating Identifiable Causal Effects through Double Machine Learning.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Causal Imitation Learning With Unobserved Confounders.

Junzhe Zhang, Daniel Kumor, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe.

Sanghack Lee, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Causal Effects via Weighted Empirical Risk Minimization.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning.

Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

General Transportability of Soft Interventions: Completeness Results.

Juan D. Correa, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Effect Identifiability under Partial-Observability.

Sanghack Lee, Elias Bareinboim

Proceedings of the 37th International Conference on Machine Learning, 2020

Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets.

Daniel Kumor, Carlos Cinelli, Elias Bareinboim

Proceedings of the 37th International Conference on Machine Learning, 2020

Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations.

George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang

Proceedings of the AMIA 2020, 2020

General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains.

Sanghack Lee, Juan D. Correa, Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Identifiability from a Combination of Observations and Experiments.

Sanghack Lee, Juan D. Correa, Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Estimating Causal Effects Using Weighting-Based Estimators.

Yonghan Jung, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments.

Juan D. Correa, Elias Bareinboim

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

General Identifiability with Arbitrary Surrogate Experiments.

Sanghack Lee, Juan D. Correa, Elias Bareinboim

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets.

Daniel Kumor, Bryant Chen, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions.

Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Identification of Conditional Causal Effects under Markov Equivalence.

Amin Jaber, Jiji Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

From Statistical Transportability to Estimating the Effect of Stochastic Interventions.

Juan D. Correa, Elias Bareinboim

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Causal Identification under Markov Equivalence: Completeness Results.

Amin Jaber, Jiji Zhang, Elias Bareinboim

Proceedings of the 36th International Conference on Machine Learning, 2019

Adjustment Criteria for Generalizing Experimental Findings.

Juan D. Correa, Jin Tian, Elias Bareinboim

Proceedings of the 36th International Conference on Machine Learning, 2019

Sensitivity Analysis of Linear Structural Causal Models.

Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim

Proceedings of the 36th International Conference on Machine Learning, 2019

Structural Causal Bandits with Non-Manipulable Variables.

Sanghack Lee, Elias Bareinboim

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding.

Andrew Forney, Elias Bareinboim

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Identification of Causal Effects in the Presence of Selection Bias.

Juan D. Correa, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Non-Parametric Path Analysis in Structural Causal Models.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Causal Identification under Markov Equivalence.

Amin Jaber, Jiji Zhang, Elias Bareinboim

Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Equality of Opportunity in Classification: A Causal Approach.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Structural Causal Bandits: Where to Intervene?

Sanghack Lee, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams.

Amin Jaber, Jiji Zhang, Elias Bareinboim

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Budgeted Experiment Design for Causal Structure Learning.

AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim

Proceedings of the 35th International Conference on Machine Learning, 2018

Characterizing the Limits of Autonomous Systems.

Junzhe Zhang, Elias Bareinboim

Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 2018

Fairness in Decision-Making - The Causal Explanation Formula.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Generalized Adjustment Under Confounding and Selection Biases.

Juan D. Correa, Jin Tian, Elias Bareinboim

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Guest editorial: special issue on causal discovery.

Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu

Int. J. Data Sci. Anal., 2017

Experimental Design for Learning Causal Graphs with Latent Variables.

Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Transfer Learning in Multi-Armed Bandits: A Causal Approach.

Junzhe Zhang, Elias Bareinboim

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Counterfactual Data-Fusion for Online Reinforcement Learners.

Andrew Forney, Judea Pearl, Elias Bareinboim

Proceedings of the 34th International Conference on Machine Learning, 2017

Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables.

Bryant Chen, Daniel Kumor, Elias Bareinboim

Proceedings of the 34th International Conference on Machine Learning, 2017

Transfer Learning in Multi-Armed Bandit: A Causal Approach.

Junzhe Zhang, Elias Bareinboim

Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems, 2017

Causal Effect Identification by Adjustment under Confounding and Selection Biases.

Juan D. Correa, Elias Bareinboim

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Preface to the ACM TIST Special Issue on Causal Discovery and Inference.

Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl

ACM Trans. Intell. Syst. Technol., 2016

Causal inference and the data-fusion problem.

Elias Bareinboim, Judea Pearl

Proc. Natl. Acad. Sci. USA, 2016

AI's 10 to Watch.

Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh

IEEE Intell. Syst., 2016

Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables.

Bryant Chen, Judea Pearl, Elias Bareinboim

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

External Validity: From Do-Calculus to Transportability Across Populations.

Judea Pearl, Elias Bareinboim

CoRR, 2015

Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models.

Bryant Chen, Judea Pearl, Elias Bareinboim

CoRR, 2015

Bandits with Unobserved Confounders: A Causal Approach.

Elias Bareinboim, Andrew Forney, Judea Pearl

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Recovering Causal Effects from Selection Bias.

Elias Bareinboim, Jin Tian

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Generalizability in Causal Inference: Theory and Algorithms.

Elias Bareinboim

PhD thesis, 2014

Generalizing causal knowledge: theory and algorithms.

Elias Bareinboim, Judea Pearl

AI Matters, 2014

Transportability from Multiple Environments with Limited Experiments: Completeness Results.

Elias Bareinboim, Judea Pearl

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Recovering from Selection Bias in Causal and Statistical Inference.

Elias Bareinboim, Jin Tian, Judea Pearl

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

A General Algorithm for Deciding Transportability of Experimental Results.

Elias Bareinboim, Judea Pearl

CoRR, 2013

Transportability from Multiple Environments with Limited Experiments.

Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Meta-Transportability of Causal Effects: A Formal Approach.

Elias Bareinboim, Judea Pearl

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

Causal Transportability with Limited Experiments.

Elias Bareinboim, Judea Pearl

Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012

Controlling Selection Bias in Causal Inference.

Elias Bareinboim, Judea Pearl

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Causal Inference by Surrogate Experiments: z-Identifiability.

Elias Bareinboim, Judea Pearl

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Transportability of Causal Effects: Completeness Results.

Elias Bareinboim, Judea Pearl

Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011

Analyzing marginal cases in differential shotgun proteomics.

Paulo C. Carvalho, Juliana S. G. Fischer, Jonas Perales, John R. Yates III, Valmir Carneiro Barbosa, Elias Bareinboim

Bioinform., 2011

Transportability of Causal and Statistical Relations: A Formal Approach.

Judea Pearl, Elias Bareinboim

Proceedings of the Data Mining Workshops (ICDMW), 2011

Local Characterizations of Causal Bayesian Networks.

Elias Bareinboim, Carlos Brito, Judea Pearl

Proceedings of the Graph Structures for Knowledge Representation and Reasoning, 2011