Elias Bareinboim

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 101

Top Venues

NeurIPS

CoRR

AAAI

ICML

University of California, Los Angeles, USA (PHD)

Education History

University of California, Los Angeles, USA

PHD

Publications

Drago Plecko, Elias Bareinboim
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making. CoRR (2024)
Drago Plecko, Elias Bareinboim
Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. Found. Trends Mach. Learn. 17 (3) (2024)
Drago Plecko, Elias Bareinboim
Reconciling Predictive and Statistical Parity: A Causal Approach. AAAI (2024)
Kevin Xia, Elias Bareinboim
Neural Causal Abstractions. CoRR (2024)
Kasra Jalaldoust, Elias Bareinboim
Transportable Representations for Domain Generalization. AAAI (2024)
Drago Plecko, Elias Bareinboim
Fairness-Accuracy Trade-Offs: A Causal Perspective. CoRR (2024)
Kevin Xia, Elias Bareinboim
Neural Causal Abstractions. AAAI (2024)
Alexis Bellot, Junzhe Zhang, Elias Bareinboim
Scores for Learning Discrete Causal Graphs with Unobserved Confounders. AAAI (2024)
Yushu Pan, Elias Bareinboim
Counterfactual Image Editing. CoRR (2024)
Shalmali Joshi, Junzhe Zhang, Elias Bareinboim
Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. AAAI (2024)
Mingxuan Li, Junzhe Zhang, Elias Bareinboim
Causally Aligned Curriculum Learning. ICLR (2024)
Negar Kiyavash, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak
Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory 4 (2023)
Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim
Causal Imitation Learning via Inverse Reinforcement Learning. ICLR (2023)
Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim
Neural Causal Models for Counterfactual Identification and Estimation. ICLR (2023)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Joint Treatment Effects by Combining Multiple Experiments. ICML (2023)
Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS (2023)
Drago Plecko, Elias Bareinboim
Reconciling Predictive and Statistical Parity: A Causal Approach. CoRR (2023)
Drago Plecko, Elias Bareinboim
Causal Fairness for Outcome Control. CoRR (2023)
Adam Li, Amin Jaber, Elias Bareinboim
Causal discovery from observational and interventional data across multiple environments. NeurIPS (2023)
Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
Causal Effect Identification in Cluster DAGs. AAAI (2023)
Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. NeurIPS (2023)
Drago Plecko, Elias Bareinboim
Causal Fairness for Outcome Control. NeurIPS (2023)
Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR (2023)
Drago Plecko, Elias Bareinboim
A Causal Framework for Decomposing Spurious Variations. CoRR (2023)
Drago Plecko, Elias Bareinboim
A Causal Framework for Decomposing Spurious Variations. NeurIPS (2023)
Junzhe Zhang, Elias Bareinboim
Can Humans Be out of the Loop? CLeaR (2022)
Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
On Measuring Causal Contributions via do-interventions. ICML (2022)
Daniel Kumor, Junzhe Zhang, Elias Bareinboim
Sequential Causal Imitation Learning with Unobserved Confounders. CoRR (2022)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Counterfactual Transportability: A Formal Approach. ICML (2022)
Junzhe Zhang, Elias Bareinboim
Online Reinforcement Learning for Mixed Policy Scopes. NeurIPS (2022)
Hyunchai Jeong, Jin Tian, Elias Bareinboim
Finding and Listing Front-door Adjustment Sets. CoRR (2022)
Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick
Causal Transportability for Visual Recognition. CVPR (2022)
Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. NeurIPS (2022)
Junzhe Zhang, Daniel Kumor, Elias Bareinboim
Causal Imitation Learning with Unobserved Confounders. CoRR (2022)
Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick
Causal Transportability for Visual Recognition. CoRR (2022)
Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
Effect Identification in Cluster Causal Diagrams. CoRR (2022)
Kevin Xia, Yushu Pan, Elias Bareinboim
Neural Causal Models for Counterfactual Identification and Estimation. CoRR (2022)
Drago Plecko, Elias Bareinboim
Causal Fairness Analysis. CoRR (2022)
Hyunchai Jeong, Jin Tian, Elias Bareinboim
Finding and Listing Front-door Adjustment Sets. NeurIPS (2022)
Junzhe Zhang, Jin Tian, Elias Bareinboim
Partial Counterfactual Identification from Observational and Experimental Data. ICML (2022)
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
The Causal-Neural Connection: Expressiveness, Learnability, and Inference. CoRR (2021)
Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafillou, Elias Bareinboim, Huan Liu, Negar Kiyavash
The KDD 2021 Workshop on Causal Discovery (CD2021). KDD (2021)
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
The Causal-Neural Connection: Expressiveness, Learnability, and Inference. NeurIPS (2021)
Daniel Kumor, Junzhe Zhang, Elias Bareinboim
Sequential Causal Imitation Learning with Unobserved Confounders. NeurIPS (2021)
Junzhe Zhang, Jin Tian, Elias Bareinboim
Partial Counterfactual Identification from Observational and Experimental Data. CoRR (2021)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. NeurIPS (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. NeurIPS (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. ICML (2021)
Junzhe Zhang, Elias Bareinboim
Bounding Causal Effects on Continuous Outcome. AAAI (2021)
Juan D. Correa, Sanghack Lee, Elias Bareinboim
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. CoRR (2021)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Identifiable Causal Effects through Double Machine Learning. AAAI (2021)
Sanghack Lee, Elias Bareinboim
Causal Identification with Matrix Equations. NeurIPS (2021)
Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash
Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. CD@KDD (2021)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. AAAI (2020)
Yonghan Jung, Jin Tian, Elias Bareinboim
Learning Causal Effects via Weighted Empirical Risk Minimization. NeurIPS (2020)
Yonghan Jung, Jin Tian, Elias Bareinboim
Estimating Causal Effects Using Weighting-Based Estimators. AAAI (2020)
Juan D. Correa, Elias Bareinboim
A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments. AAAI (2020)
Juan D. Correa, Elias Bareinboim
General Transportability of Soft Interventions: Completeness Results. NeurIPS (2020)
Sanghack Lee, Elias Bareinboim
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. NeurIPS (2020)
Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. NeurIPS (2020)
Daniel Kumor, Carlos Cinelli, Elias Bareinboim
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. ICML (2020)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
Identifiability from a Combination of Observations and Experiments. AAAI (2020)
Sanghack Lee, Elias Bareinboim
Causal Effect Identifiability under Partial-Observability. ICML (2020)
Junzhe Zhang, Daniel Kumor, Elias Bareinboim
Causal Imitation Learning With Unobserved Confounders. NeurIPS (2020)
George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang
Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA (2020)
Junzhe Zhang, Elias Bareinboim
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. NeurIPS (2019)
Sanghack Lee, Juan D. Correa, Elias Bareinboim
General Identifiability with Arbitrary Surrogate Experiments. UAI (2019)
Sanghack Lee, Elias Bareinboim
Structural Causal Bandits with Non-Manipulable Variables. AAAI (2019)
Juan D. Correa, Elias Bareinboim
From Statistical Transportability to Estimating the Effect of Stochastic Interventions. IJCAI (2019)
Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. NeurIPS (2019)
Amin Jaber, Jiji Zhang, Elias Bareinboim
Causal Identification under Markov Equivalence: Completeness Results. ICML (2019)
Amin Jaber, Jiji Zhang, Elias Bareinboim
On Causal Identification under Markov Equivalence. IJCAI (2019)
Andrew Forney, Elias Bareinboim
Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. AAAI (2019)
Amin Jaber, Jiji Zhang, Elias Bareinboim
Identification of Conditional Causal Effects under Markov Equivalence. NeurIPS (2019)
Daniel Kumor, Bryant Chen, Elias Bareinboim
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. CoRR (2019)
Juan D. Correa, Jin Tian, Elias Bareinboim
Identification of Causal Effects in the Presence of Selection Bias. AAAI (2019)
Daniel Kumor, Bryant Chen, Elias Bareinboim
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. NeurIPS (2019)
Juan D. Correa, Jin Tian, Elias Bareinboim
Adjustment Criteria for Generalizing Experimental Findings. ICML (2019)
Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim
Sensitivity Analysis of Linear Structural Causal Models. ICML (2019)
Junzhe Zhang, Elias Bareinboim
Equality of Opportunity in Classification: A Causal Approach. NeurIPS (2018)
Junzhe Zhang, Elias Bareinboim
Fairness in Decision-Making - The Causal Explanation Formula. AAAI (2018)
Junzhe Zhang, Elias Bareinboim
Characterizing the Limits of Autonomous Systems. AAMAS (2018)
Amin Jaber, Jiji Zhang, Elias Bareinboim
A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams. IJCAI (2018)
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
Budgeted Experiment Design for Causal Structure Learning. ICML (2018)
Sanghack Lee, Elias Bareinboim
Structural Causal Bandits: Where to Intervene? NeurIPS (2018)
Amin Jaber, Jiji Zhang, Elias Bareinboim
Causal Identification under Markov Equivalence. UAI (2018)
Junzhe Zhang, Elias Bareinboim
Non-Parametric Path Analysis in Structural Causal Models. UAI (2018)
Amin Jaber, Jiji Zhang, Elias Bareinboim
Causal Identification under Markov Equivalence. CoRR (2018)
Juan D. Correa, Jin Tian, Elias Bareinboim
Generalized Adjustment Under Confounding and Selection Biases. AAAI (2018)
Junzhe Zhang, Elias Bareinboim
Transfer Learning in Multi-Armed Bandits: A Causal Approach. IJCAI (2017)
Junzhe Zhang, Elias Bareinboim
Transfer Learning in Multi-Armed Bandit: A Causal Approach. AAMAS (2017)
Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu
Guest editorial: special issue on causal discovery. Int. J. Data Sci. Anal. 3 (2) (2017)
Bryant Chen, Daniel Kumor, Elias Bareinboim
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. ICML (2017)
Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
Experimental Design for Learning Causal Graphs with Latent Variables. NIPS (2017)
Juan D. Correa, Elias Bareinboim
Causal Effect Identification by Adjustment under Confounding and Selection Biases. AAAI (2017)
Andrew Forney, Judea Pearl, Elias Bareinboim
Counterfactual Data-Fusion for Online Reinforcement Learners. ICML (2017)
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
Budgeted Experiment Design for Causal Structure Learning. CoRR (2017)
Elias Bareinboim, Judea Pearl
Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. USA 113 (27) (2016)
Bryant Chen, Judea Pearl, Elias Bareinboim
Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables. IJCAI (2016)
Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl
Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 7 (2) (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Elias Bareinboim