← Authors

Elias Bareinboim

82 papers · 2012–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸƒ Academic Marathon (13) πŸ—ΊοΈ Taxonomy Completionist (57) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (9) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (8) 🏠 Conference Loyalist (33) 🌟 Keyword Trendsetter Combo (3) 🀝 Dynamic Duo (16) πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (10) πŸ† Keyword Champion (2) πŸ† Grand Slam 🌱 Topic Pioneer πŸ”₯ Unstoppable (14) πŸ“ˆ Trend Setter ❓ The Questioner (2) πŸš€ Conference Pioneer ⚑ Prolific Year (12) πŸ—ƒοΈ Keyword Collector (216) πŸ’Ž Century Club (82)

Conferences

NIPS (33) AAAI (17) ICML (17) IJCAI (5) ICLR (4) AISTATS (2) UAI (2) CLEAR (1) CVPR (1)

Top co-authors

Junzhe Zhang (16) Jin Tian (15) Sanghack Lee (10) Yonghan Jung (9) Judea Pearl (8) Amin Jaber (8) Daniel Kumor (6) Drago Plecko (6) Jiji Zhang (5) Juan Correa (5)

Keywords

causal inference (55) causal effect (14) causal identification (13) graphical model (9) causal graph (8) structural causal model (7) observational datum (6) unobserved confounder (6) multi-armed bandit (5) causal effect estimation (5) instrumental variable (5) markov equivalence (4) domain adaptation (4) observational study (4) counterfactual reasoning (4) algorithmic fairness (4) causal discovery (4) domain generalization (4) causal effect identification (4) reinforcement learning (4)

Papers

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