Elias Bareinboim

Automatic Reward Shaping from Confounded Offline Data ICML 2025 Causal Abstraction Inference under Lossy Representations ICML 2025 Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation UAI 2025 Counterfactual Graphical Models: Constraints and Inference ICML 2025 Counterfactual Identification Under Monotonicity Constraints AAAI 2025 Counterfactual Realizability ICLR 2025 Fairness-Accuracy Trade-Offs: A Causal Perspective AAAI 2025 Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies AAAI 2025 Causal Imitation for Markov Decision Processes: a Partial Identification Approach NeurIPS 2024 Causally Aligned Curriculum Learning ICLR 2024 Counterfactual Image Editing ICML 2024 Disentangled Representation Learning in Non-Markovian Causal Systems NeurIPS 2024 Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making NeurIPS 2024 Neural Causal Abstractions AAAI 2024 Partial Transportability for Domain Generalization NeurIPS 2024 Reconciling Predictive and Statistical Parity: A Causal Approach AAAI 2024 Scores for Learning Discrete Causal Graphs with Unobserved Confounders AAAI 2024 Towards Safe Policy Learning under Partial Identifiability: A Causal Approach AAAI 2024 Transportable Representations for Domain Generalization AAAI 2024 Unified Covariate Adjustment for Causal Inference NeurIPS 2024 A Causal Framework for Decomposing Spurious Variations NeurIPS 2023 Causal discovery from observational and interventional data across multiple environments NeurIPS 2023 Causal Effect Identification in Cluster DAGs AAAI 2023 Causal Fairness for Outcome Control NeurIPS 2023 Causal Imitation Learning via Inverse Reinforcement Learning ICLR 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments NeurIPS 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments ICML 2023 Neural Causal Models for Counterfactual Identification and Estimation ICLR 2023 Nonparametric Identifiability of Causal Representations from Unknown Interventions NeurIPS 2023 Can Humans Be out of the Loop? CLeaR 2022 Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness NeurIPS 2022 Causal Transportability for Visual Recognition CVPR 2022 Counterfactual Transportability: A Formal Approach ICML 2022 Finding and Listing Front-door Adjustment Sets NeurIPS 2022 Online Reinforcement Learning for Mixed Policy Scopes NeurIPS 2022 On Measuring Causal Contributions via do-interventions ICML 2022 Partial Counterfactual Identification from Observational and Experimental Data ICML 2022 Bounding Causal Effects on Continuous Outcome AAAI 2021 Causal Identification with Matrix Equations NeurIPS 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments NeurIPS 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning ICML 2021 Estimating Identifiable Causal Effects through Double Machine Learning AAAI 2021 Nested Counterfactual Identification from Arbitrary Surrogate Experiments NeurIPS 2021 Sequential Causal Imitation Learning with Unobserved Confounders NeurIPS 2021 The Causal-Neural Connection: Expressiveness, Learnability, and Inference NeurIPS 2021 A Calculus for Stochastic Interventions:Causal Effect Identification and Surrogate Experiments AAAI 2020 Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning NeurIPS 2020 Causal Effect Identifiability under Partial-Observability ICML 2020 Causal Imitation Learning With Unobserved Confounders NeurIPS 2020 Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe NeurIPS 2020 Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets ICML 2020 Estimating Causal Effects Using Weighting-Based Estimators AAAI 2020 General Transportability of Soft Interventions: Completeness Results NeurIPS 2020 General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains AAAI 2020 Learning Causal Effects via Weighted Empirical Risk Minimization NeurIPS 2020 Adjustment Criteria for Generalizing Experimental Findings ICML 2019 Causal Identification under Markov Equivalence: Completeness Results ICML 2019 Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions NeurIPS 2019 Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding AAAI 2019 Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets NeurIPS 2019 From Statistical Transportability to Estimating the Effect of Stochastic Interventions IJCAI 2019 General Identifiability with Arbitrary Surrogate Experiments UAI 2019 Identification of Causal Effects in the Presence of Selection Bias AAAI 2019 Identification of Conditional Causal Effects under Markov Equivalence NeurIPS 2019 Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes NeurIPS 2019 On Causal Identification under Markov Equivalence IJCAI 2019 Sensitivity Analysis of Linear Structural Causal Models ICML 2019 Structural Causal Bandits with Non-Manipulable Variables AAAI 2019 A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams IJCAI 2018 Budgeted Experiment Design for Causal Structure Learning ICML 2018 Equality of Opportunity in Classification: A Causal Approach NeurIPS 2018 Structural Causal Bandits: Where to Intervene? NeurIPS 2018 Counterfactual Data-Fusion for Online Reinforcement Learners ICML 2017 Experimental Design for Learning Causal Graphs with Latent Variables NeurIPS 2017 Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables ICML 2017 Transfer Learning in Multi-Armed Bandits: A Causal Approach IJCAI 2017 Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables IJCAI 2016 Bandits with Unobserved Confounders: A Causal Approach NeurIPS 2015 Transportability from Multiple Environments with Limited Experiments: Completeness Results NeurIPS 2014 Meta-Transportability of Causal Effects: A Formal Approach AISTATS 2013 Transportability from Multiple Environments with Limited Experiments NeurIPS 2013 Controlling Selection Bias in Causal Inference AISTATS 2012