Bareinboim, Elias

104 publications

NeurIPS 2025 A Hierarchy of Graphical Models for Counterfactual Inferences Hongshuo Yang, Elias Bareinboim

ICML 2025 Automatic Reward Shaping from Confounded Offline Data Mingxuan Li, Junzhe Zhang, Elias Bareinboim

ICML 2025 Causal Abstraction Inference Under Lossy Representations Kevin Muyuan Xia, Elias Bareinboim

NeurIPS 2025 Causal Discovery over Clusters of Variables in Markovian Systems Tara Vafai Anand, Adèle H. Ribeiro, Jin Tian, George Hripcsak, Elias Bareinboim

UAI 2025 Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation Junzhe Zhang, Elias Bareinboim

NeurIPS 2025 Confounding Robust Deep Reinforcement Learning: A Causal Approach Mingxuan Li, Junzhe Zhang, Elias Bareinboim

ICML 2025 Counterfactual Graphical Models: Constraints and Inference Juan D. Correa, Elias Bareinboim

AAAI 2025 Counterfactual Identification Under Monotonicity Constraints Aurghya Maiti, Drago Plecko, Elias Bareinboim

NeurIPS 2025 Counterfactual Image Editing with Disentangled Causal Latent Space Yushu Pan, Elias Bareinboim

ICLR 2025 Counterfactual Realizability Arvind Raghavan, Elias Bareinboim

AAAI 2025 Fairness-Accuracy Trade-Offs: A Causal Perspective Drago Plecko, Elias Bareinboim

NeurIPS 2025 From Black-Box to Causal-Box: Towards Building More Interpretable Models Inwoo Hwang, Yushu Pan, Elias Bareinboim

NeurIPS 2025 Less Greedy Equivalence Search Adiba Ejaz, Elias Bareinboim

NeurIPS 2025 Structural Causal Bandits Under Markov Equivalence Min Woo Park, Andy Arditi, Elias Bareinboim, Sanghack Lee

AAAI 2025 Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim

FnTML 2024 Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning Drago Plecko, Elias Bareinboim

NeurIPS 2024 Causal Imitation for Markov Decision Processes: A Partial Identification Approach Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim

ICLR 2024 Causally Aligned Curriculum Learning Mingxuan Li, Junzhe Zhang, Elias Bareinboim

ICML 2024 Counterfactual Image Editing Yushu Pan, Elias Bareinboim

NeurIPS 2024 Disentangled Representation Learning in Non-Markovian Causal Systems Adam Li, Yushu Pan, Elias Bareinboim

NeurIPS 2024 Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making Drago Plecko, Elias Bareinboim

AAAI 2024 Neural Causal Abstractions Kevin Xia, Elias Bareinboim

NeurIPS 2024 Partial Transportability for Domain Generalization Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim

AAAI 2024 Reconciling Predictive and Statistical Parity: A Causal Approach Drago Plecko, Elias Bareinboim

AAAI 2024 Scores for Learning Discrete Causal Graphs with Unobserved Confounders Alexis Bellot, Junzhe Zhang, Elias Bareinboim

AAAI 2024 Towards Safe Policy Learning Under Partial Identifiability: A Causal Approach Shalmali Joshi, Junzhe Zhang, Elias Bareinboim

AAAI 2024 Transportable Representations for Domain Generalization Kasra Jalaldoust, Elias Bareinboim

NeurIPS 2024 Unified Covariate Adjustment for Causal Inference Yonghan Jung, Jin Tian, Elias Bareinboim

NeurIPS 2023 A Causal Framework for Decomposing Spurious Variations Drago Plecko, Elias Bareinboim

NeurIPS 2023 Causal Discovery from Observational and Interventional Data Across Multiple Environments Adam Li, Amin Jaber, Elias Bareinboim

AAAI 2023 Causal Effect Identification in Cluster DAGs Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim

NeurIPS 2023 Causal Fairness for Outcome Control Drago Plecko, Elias Bareinboim

ICLR 2023 Causal Imitation Learning via Inverse Reinforcement Learning Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim

NeurIPS 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim

ICML 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments Yonghan Jung, Jin Tian, Elias Bareinboim

ICLR 2023 Neural Causal Models for Counterfactual Identification and Estimation Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim

NeurIPS 2023 Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen, Michel Besserve, Liang Wendong, Luigi Gresele, Armin Kekić, Elias Bareinboim, David M. Blei, Bernhard Schölkopf

CLeaR 2022 Can Humans Be Out of the Loop? Junzhe Zhang, Elias Bareinboim

NeurIPS 2022 Causal Identification Under Markov Equivalence: Calculus, Algorithm, and Completeness Amin Jaber, Adele Ribeiro, Jiji Zhang, Elias Bareinboim

CVPR 2022 Causal Transportability for Visual Recognition Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick

ICML 2022 Counterfactual Transportability: A Formal Approach Juan D Correa, Sanghack Lee, Elias Bareinboim

NeurIPS 2022 Finding and Listing Front-Door Adjustment Sets Hyunchai Jeong, Jin Tian, Elias Bareinboim

ICML 2022 On Measuring Causal Contributions via Do-Interventions Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim

NeurIPS 2022 Online Reinforcement Learning for Mixed Policy Scopes Junzhe Zhang, Elias Bareinboim

ICML 2022 Partial Counterfactual Identification from Observational and Experimental Data Junzhe Zhang, Jin Tian, Elias Bareinboim

AAAI 2021 Bounding Causal Effects on Continuous Outcome Junzhe Zhang, Elias Bareinboim

NeurIPS 2021 Causal Identification with Matrix Equations Sanghack Lee, Elias Bareinboim

NeurIPS 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2021 Estimating Identifiable Causal Effects Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

NeurIPS 2021 Nested Counterfactual Identification from Arbitrary Surrogate Experiments Juan Correa, Sanghack Lee, Elias Bareinboim

NeurIPS 2021 Sequential Causal Imitation Learning with Unobserved Confounders Daniel Kumor, Junzhe Zhang, Elias Bareinboim

NeurIPS 2021 The Causal-Neural Connection: Expressiveness, Learnability, and Inference Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim

AAAI 2020 A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments Juan D. Correa, Elias Bareinboim

NeurIPS 2020 Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

ICML 2020 Causal Effect Identifiability Under Partial-Observability Sanghack Lee, Elias Bareinboim

NeurIPS 2020 Causal Imitation Learning with Unobserved Confounders Junzhe Zhang, Daniel Kumor, Elias Bareinboim

NeurIPS 2020 Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe Sanghack Lee, Elias Bareinboim

ICML 2020 Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets Daniel Kumor, Carlos Cinelli, Elias Bareinboim

AAAI 2020 Estimating Causal Effects Using Weighting-Based Estimators Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2020 General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains Sanghack Lee, Juan D. Correa, Elias Bareinboim

NeurIPS 2020 General Transportability of Soft Interventions: Completeness Results Juan Correa, Elias Bareinboim

AAAI 2020 Identifiability from a Combination of Observations and Experiments Sanghack Lee, Juan D. Correa, Elias Bareinboim

NeurIPS 2020 Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2019 Adjustment Criteria for Generalizing Experimental Findings Juan Correa, Jin Tian, Elias Bareinboim

ICML 2019 Causal Identification Under Markov Equivalence: Completeness Results Amin Jaber, Jiji Zhang, Elias Bareinboim

NeurIPS 2019 Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim

AAAI 2019 Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding Andrew Forney, Elias Bareinboim

NeurIPS 2019 Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets Daniel Kumor, Bryant Chen, Elias Bareinboim

IJCAI 2019 From Statistical Transportability to Estimating the Effect of Stochastic Interventions Juan D. Correa, Elias Bareinboim

UAI 2019 General Identifiability with Arbitrary Surrogate Experiments Sanghack Lee, Juan D. Correa, Elias Bareinboim

AAAI 2019 Identification of Causal Effects in the Presence of Selection Bias Juan D. Correa, Jin Tian, Elias Bareinboim

NeurIPS 2019 Identification of Conditional Causal Effects Under Markov Equivalence Amin Jaber, Jiji Zhang, Elias Bareinboim

NeurIPS 2019 Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes Junzhe Zhang, Elias Bareinboim

IJCAI 2019 On Causal Identification Under Markov Equivalence Amin Jaber, Jiji Zhang, Elias Bareinboim

ICML 2019 Sensitivity Analysis of Linear Structural Causal Models Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim

AAAI 2019 Structural Causal Bandits with Non-Manipulable Variables Sanghack Lee, Elias Bareinboim

IJCAI 2018 A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams Amin Jaber, Jiji Zhang, Elias Bareinboim

ICML 2018 Budgeted Experiment Design for Causal Structure Learning AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim

UAI 2018 Causal Identification Under Markov Equivalence Amin Jaber, Jiji Zhang, Elias Bareinboim

NeurIPS 2018 Equality of Opportunity in Classification: A Causal Approach Junzhe Zhang, Elias Bareinboim

AAAI 2018 Fairness in Decision-Making - The Causal Explanation Formula Junzhe Zhang, Elias Bareinboim

AAAI 2018 Generalized Adjustment Under Confounding and Selection Biases Juan D. Correa, Jin Tian, Elias Bareinboim

UAI 2018 Non-Parametric Path Analysis in Structural Causal Models Junzhe Zhang, Elias Bareinboim

NeurIPS 2018 Structural Causal Bandits: Where to Intervene? Sanghack Lee, Elias Bareinboim

AAAI 2017 Causal Effect Identification by Adjustment Under Confounding and Selection Biases Juan D. Correa, Elias Bareinboim

ICML 2017 Counterfactual Data-Fusion for Online Reinforcement Learners Andrew Forney, Judea Pearl, Elias Bareinboim

NeurIPS 2017 Experimental Design for Learning Causal Graphs with Latent Variables Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim

ICML 2017 Identification and Model Testing in Linear Structural Equation Models Using Auxiliary Variables Bryant Chen, Daniel Kumor, Elias Bareinboim

IJCAI 2017 Transfer Learning in Multi-Armed Bandits: A Causal Approach Junzhe Zhang, Elias Bareinboim

IJCAI 2016 Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables Bryant Chen, Judea Pearl, Elias Bareinboim

UAI 2016 Proceedings of the UAI 2016 Workshop on Causation: Foundation to Application Co-Located with the 32nd Conference on Uncertainty in Artificial Intelligence (UAI 2016), Jersey City, USA, June 29, 2016 Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva

NeurIPS 2015 Bandits with Unobserved Confounders: A Causal Approach Elias Bareinboim, Andrew Forney, Judea Pearl

AAAI 2015 Recovering Causal Effects from Selection Bias Elias Bareinboim, Jin Tian

AAAI 2014 Recovering from Selection Bias in Causal and Statistical Inference Elias Bareinboim, Jin Tian, Judea Pearl

NeurIPS 2014 Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl

AAAI 2013 Causal Transportability with Limited Experiments Elias Bareinboim, Judea Pearl

AISTATS 2013 Meta-Transportability of Causal Effects: A Formal Approach Elias Bareinboim, Judea Pearl

NeurIPS 2013 Transportability from Multiple Environments with Limited Experiments Elias Bareinboim, Sanghack Lee, Vasant Honavar, Judea Pearl

UAI 2012 Causal Inference by Surrogate Experiments: Z-Identifiability Elias Bareinboim, Judea Pearl

AISTATS 2012 Controlling Selection Bias in Causal Inference Elias Bareinboim, Judea Pearl

AAAI 2012 Transportability of Causal Effects: Completeness Results Elias Bareinboim, Judea Pearl

AAAI 2011 Controlling Selection Bias in Causal Inference Elias Bareinboim, Judea Pearl

AAAI 2011 Transportability of Causal and Statistical Relations: A Formal Approach Judea Pearl, Elias Bareinboim