Elias Bareinboim

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