Transportability of Causal and Statistical Relations: A Formal Approach. - Judea Pearl; Elias Bareinboim (AAAI, 2011)

Local Characterizations of Causal Bayesian Networks. - Elias Bareinboim; Carlos Brito 001; Judea Pearl (GKR, 2011)

Analyzing marginal cases in differential shotgun proteomics. - Paulo C. Carvalho; Juliana S. G. Fischer; Jonas Perales; John R. Yates III; Valmir Carneiro Barbosa; Elias Bareinboim (Bioinform., 2011)

Transportability of Causal Effects: Completeness Results. - Elias Bareinboim; Judea Pearl (AAAI, 2012)

Causal Inference by Surrogate Experiments: z-Identifiability. - Elias Bareinboim; Judea Pearl (UAI, 2012)

Controlling Selection Bias in Causal Inference. - Elias Bareinboim; Judea Pearl (AISTATS, 2012)

Causal Transportability with Limited Experiments. - Elias Bareinboim; Judea Pearl (AAAI, 2013)

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

Transportability from Multiple Environments with Limited Experiments. - Elias Bareinboim; Sanghack Lee; Vasant G. Honavar; Judea Pearl (NIPS, 2013)

A General Algorithm for Deciding Transportability of Experimental Results. - Elias Bareinboim; Judea Pearl (CoRR, 2013)

Recovering from Selection Bias in Causal and Statistical Inference. - Elias Bareinboim; Jin Tian 001; Judea Pearl (AAAI, 2014)

Transportability from Multiple Environments with Limited Experiments: Completeness Results. - Elias Bareinboim; Judea Pearl (NIPS, 2014)

Generalizing causal knowledge: theory and algorithms. - Elias Bareinboim; Judea Pearl (AI Matters, 2014)

Generalizability in Causal Inference: Theory and Algorithms. - Elias Bareinboim (2014)

Recovering Causal Effects from Selection Bias. - Elias Bareinboim; Jin Tian 001 (AAAI, 2015)

Bandits with Unobserved Confounders: A Causal Approach. - Elias Bareinboim; Andrew Forney; Judea Pearl (NIPS, 2015)

Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models. - Bryant Chen; Judea Pearl; Elias Bareinboim (CoRR, 2015)

External Validity: From Do-Calculus to Transportability Across Populations. - Judea Pearl; Elias Bareinboim (CoRR, 2015)

Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables. - Bryant Chen; Judea Pearl; Elias Bareinboim (IJCAI, 2016)

AI's 10 to Watch. - Haris Aziz 001; Elias Bareinboim; Yejin Choi 001; Daniel J. Hsu; Shivaram Kalyanakrishnan; Reshef Meir; Suchi Saria; Gerardo I. Simari; Lirong Xia; William Yeoh 001 (IEEE Intell. Syst., 2016)

Causal inference and the data-fusion problem. - Elias Bareinboim; Judea Pearl (Proc. Natl. Acad. Sci. USA, 2016)

Preface to the ACM TIST Special Issue on Causal Discovery and Inference. - Kun Zhang 001; Jiuyong Li; Elias Bareinboim; Bernhard Schölkopf; Judea Pearl (ACM Trans. Intell. Syst. Technol., 2016)

Causal Effect Identification by Adjustment under Confounding and Selection Biases. - Juan D. Correa; Elias Bareinboim (AAAI, 2017)

Transfer Learning in Multi-Armed Bandit: A Causal Approach. - Junzhe Zhang 001; Elias Bareinboim (AAMAS, 2017)

Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. - Bryant Chen; Daniel Kumor; Elias Bareinboim (ICML, 2017)

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

Transfer Learning in Multi-Armed Bandits: A Causal Approach. - Junzhe Zhang 001; Elias Bareinboim (IJCAI, 2017)

Experimental Design for Learning Causal Graphs with Latent Variables. - Murat Kocaoglu; Karthikeyan Shanmugam; Elias Bareinboim (NIPS, 2017)

Guest editorial: special issue on causal discovery. - Jiuyong Li; Kun Zhang 001; Elias Bareinboim; Lin Liu 003 (Int. J. Data Sci. Anal., 2017)

Generalized Adjustment Under Confounding and Selection Biases. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (AAAI, 2018)

Fairness in Decision-Making - The Causal Explanation Formula. - Junzhe Zhang 001; Elias Bareinboim (AAAI, 2018)

Characterizing the Limits of Autonomous Systems. - Junzhe Zhang 001; Elias Bareinboim (AAMAS, 2018)

Budgeted Experiment Design for Causal Structure Learning. - AmirEmad Ghassami; Saber Salehkaleybar; Negar Kiyavash; Elias Bareinboim (ICML, 2018)

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

Structural Causal Bandits: Where to Intervene? - Sanghack Lee; Elias Bareinboim (NeurIPS, 2018)

Equality of Opportunity in Classification: A Causal Approach. - Junzhe Zhang 001; Elias Bareinboim (NeurIPS, 2018)

Non-Parametric Path Analysis in Structural Causal Models. - Junzhe Zhang 001; Elias Bareinboim (UAI, 2018)

Identification of Causal Effects in the Presence of Selection Bias. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (AAAI, 2019)

Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. - Andrew Forney; Elias Bareinboim (AAAI, 2019)

Structural Causal Bandits with Non-Manipulable Variables. - Sanghack Lee; Elias Bareinboim (AAAI, 2019)

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

Adjustment Criteria for Generalizing Experimental Findings. - Juan D. Correa; Jin Tian 001; Elias Bareinboim (ICML, 2019)

Causal Identification under Markov Equivalence: Completeness Results. - Amin Jaber; Jiji Zhang; Elias Bareinboim (ICML, 2019)

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

On Causal Identification under Markov Equivalence. - Amin Jaber; Jiji Zhang; Elias Bareinboim (IJCAI, 2019)

Identification of Conditional Causal Effects under Markov Equivalence. - 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 (NeurIPS, 2019)

Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. - Daniel Kumor; Bryant Chen; Elias Bareinboim (NeurIPS, 2019)

Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. - Junzhe Zhang 001; Elias Bareinboim (NeurIPS, 2019)

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

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

Estimating Causal Effects Using Weighting-Based Estimators. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (AAAI, 2020)

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

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

Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. - George Hripcsak; David M. Blei; Elias Bareinboim; Martijn J. Schuemie; Linying Zhang (AMIA, 2020)

Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. - Daniel Kumor; Carlos Cinelli; Elias Bareinboim (ICML, 2020)

Causal Effect Identifiability under Partial-Observability. - Sanghack Lee; Elias Bareinboim (ICML, 2020)

General Transportability of Soft Interventions: Completeness Results. - 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 (NeurIPS, 2020)

Learning Causal Effects via Weighted Empirical Risk Minimization. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (NeurIPS, 2020)

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. - Sanghack Lee; Elias Bareinboim (NeurIPS, 2020)

Causal Imitation Learning With Unobserved Confounders. - Junzhe Zhang 001; Daniel Kumor; Elias Bareinboim (NeurIPS, 2020)

Estimating Identifiable Causal Effects through Double Machine Learning. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (AAAI, 2021)

Bounding Causal Effects on Continuous Outcome. - Junzhe Zhang 001; Elias Bareinboim (AAAI, 2021)

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (ICML, 2021)

Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. - Thuc Duy Le; Jiuyong Li; Gregory F. Cooper; Sofia Triantafyllou; Elias Bareinboim; Huan Liu 001; Negar Kiyavash (CD@KDD, 2021)

The KDD 2021 Workshop on Causal Discovery (CD2021). - Thuc Duy Le; Jiuyong Li; Gregory F. Cooper; Sofia Triantafillou; Elias Bareinboim; Huan Liu 001; Negar Kiyavash (KDD, 2021)

Nested Counterfactual Identification from Arbitrary Surrogate Experiments. - Juan D. Correa; Sanghack Lee; Elias Bareinboim (NeurIPS, 2021)

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (NeurIPS, 2021)

Sequential Causal Imitation Learning with Unobserved Confounders. - Daniel Kumor; Junzhe Zhang 001; Elias Bareinboim (NeurIPS, 2021)

Causal Identification with Matrix Equations. - Sanghack Lee; Elias Bareinboim (NeurIPS, 2021)

The Causal-Neural Connection: Expressiveness, Learnability, and Inference. - Kevin Xia 001; Kai-Zhan Lee; Yoshua Bengio; Elias Bareinboim (NeurIPS, 2021)

Recovering from Selection Bias in Causal and Statistical Inference. - Elias Bareinboim; Jin Tian 001; Judea Pearl (Probabilistic and Causal Inference, 2022)

On Pearl's Hierarchy and the Foundations of Causal Inference. - Elias Bareinboim; Juan D. Correa; Duligur Ibeling; Thomas Icard (Probabilistic and Causal Inference, 2022)

External Validity: From Do-Calculus to Transportability Across Populations. - Judea Pearl; Elias Bareinboim (Probabilistic and Causal Inference, 2022)

Can Humans Be out of the Loop? - Junzhe Zhang 001; Elias Bareinboim (CLeaR, 2022)

Causal Transportability for Visual Recognition. - Chengzhi Mao; Kevin Xia 001; James Wang; Hao Wang 014; Junfeng Yang; Elias Bareinboim; Carl Vondrick (CVPR, 2022)

Counterfactual Transportability: A Formal Approach. - Juan D. Correa; Sanghack Lee; Elias Bareinboim (ICML, 2022)

On Measuring Causal Contributions via do-interventions. - Yonghan Jung; Shiva Prasad Kasiviswanathan; Jin Tian 001; Dominik Janzing; Patrick Blöbaum; Elias Bareinboim (ICML, 2022)

Partial Counterfactual Identification from Observational and Experimental Data. - Junzhe Zhang 001; Jin Tian 001; Elias Bareinboim (ICML, 2022)

Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. - Amin Jaber; Adèle H. Ribeiro; Jiji Zhang; Elias Bareinboim (NeurIPS, 2022)

Finding and Listing Front-door Adjustment Sets. - Hyunchai Jeong; Jin Tian 001; Elias Bareinboim (NeurIPS, 2022)

Online Reinforcement Learning for Mixed Policy Scopes. - Junzhe Zhang 001; Elias Bareinboim (NeurIPS, 2022)

Effect Identification in Cluster Causal Diagrams. - Tara V. Anand; Adèle H. Ribeiro; Jin Tian 001; Elias Bareinboim (CoRR, 2022)

Causal Fairness Analysis. - Drago Plecko; Elias Bareinboim (CoRR, 2022)

Causal Effect Identification in Cluster DAGs. - Tara V. Anand; Adèle H. Ribeiro; Jin Tian 001; Elias Bareinboim (AAAI, 2023)

Causal Imitation Learning via Inverse Reinforcement Learning. - Kangrui Ruan; Junzhe Zhang 001; Xuan Di; Elias Bareinboim (ICLR, 2023)

Neural Causal Models for Counterfactual Identification and Estimation. - Kevin Muyuan Xia; Yushu Pan; Elias Bareinboim (ICLR, 2023)

Estimating Joint Treatment Effects by Combining Multiple Experiments. - Yonghan Jung; Jin Tian 001; Elias Bareinboim (ICML, 2023)

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. - Yonghan Jung; Ivan Diaz; Jin Tian 001; Elias Bareinboim (NeurIPS, 2023)

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

Causal discovery from observational and interventional data across multiple environments. - Adam Li; Amin Jaber; Elias Bareinboim (NeurIPS, 2023)

A Causal Framework for Decomposing Spurious Variations. - Drago Plecko; Elias Bareinboim (NeurIPS, 2023)

Causal Fairness for Outcome Control. - Drago Plecko; Elias Bareinboim (NeurIPS, 2023)

Editorial Special Issue on Causality: Fundamental Limits and Applications. - Negar Kiyavash; Elias Bareinboim; Todd P. Coleman; Alex Dimakis; Bernhard Schlkopf; Peter Spirtes; Kun Zhang 001; Robert Nowak 001 (IEEE J. Sel. Areas Inf. Theory, 2023)

Neural Causal Abstractions. - Kevin Xia 001; Elias Bareinboim (AAAI, 2024)

Scores for Learning Discrete Causal Graphs with Unobserved Confounders. - Alexis Bellot; Junzhe Zhang 001; Elias Bareinboim (AAAI, 2024)

Transportable Representations for Domain Generalization. - Kasra Jalaldoust; Elias Bareinboim (AAAI, 2024)

Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. - Shalmali Joshi; Junzhe Zhang 001; Elias Bareinboim (AAAI, 2024)

Reconciling Predictive and Statistical Parity: A Causal Approach. - Drago Plecko; Elias Bareinboim (AAAI, 2024)

Causally Aligned Curriculum Learning. - Mingxuan Li 001; Junzhe Zhang 001; Elias Bareinboim (ICLR, 2024)

Counterfactual Image Editing. - Yushu Pan; Elias Bareinboim (ICML, 2024)

Partial Transportability for Domain Generalization. - Kasra Jalaldoust; Alexis Bellot; Elias Bareinboim (NeurIPS, 2024)

Unified Covariate Adjustment for Causal Inference. - Yonghan Jung; Jin Tian 001; 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 (NeurIPS, 2024)

Causal Imitation for Markov Decision Processes: a Partial Identification Approach. - Kangrui Ruan; Junzhe Zhang 001; Xuan Di; Elias Bareinboim (NeurIPS, 2024)

Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. - Drago Plecko; Elias Bareinboim (Found. Trends Mach. Learn., 2024)

Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. - Hyunchai Jeong; Adiba Ejaz; Jin Tian 001; Elias Bareinboim (AAAI, 2025)

Counterfactual Identification Under Monotonicity Constraints. - Aurghya Maiti; Drago Plecko; Elias Bareinboim (AAAI, 2025)

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

Counterfactual Realizability. - Arvind Raghavan; Elias Bareinboim (ICLR, 2025)

Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation. - Junzhe Zhang 001; Elias Bareinboim (UAI, 2025)

An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes. - Drago Plecko; Paul Secombe; Andrea Clarke; Amelia Fiske; Samarra Toby; Donisha Duff; David Pilcher; Leo Anthony Celi; Rinaldo Bellomo; Elias Bareinboim (CoRR, 2025)

Automatic Reward Shaping from Confounded Offline Data. - Mingxuan Li; Junzhe Zhang; Elias Bareinboim (CoRR, 2025)

Less Greedy Equivalence Search. - Adiba Ejaz; Elias Bareinboim (CoRR, 2025)