Elias Bareinboim

157 papers A* 84A 9Misc 1Journal 49Unranked 8

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Causal Flow Q-Learning for Robust Offline Reinforcement Learning. CoRR Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2026	J	`jnl`	Causal Identification from Counterfactual Data: Completeness and Bounding Results. CoRR Arvind Raghavan, Elias Bareinboim
2026	J	`jnl`	Confounding Robust Continuous Control via Automatic Reward Shaping. CoRR Mateo Juliani, Mingxuan Li, Elias Bareinboim
2025	J	`jnl`	Adapting, Fast and Slow: Transportable Circuits for Few-Shot Learning. CoRR Kasra Jalaldoust, Elias Bareinboim
2025	J	`jnl`	An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes. CoRR Drago Plecko, Paul Secombe, Andrea Clarke, Amelia Fiske, Samarra Toby, Donisha Duff, David Pilcher, Leo Anthony Celi, Rinaldo Bellomo, Elias Bareinboim
2025	A*	`conf`	Automatic Reward Shaping from Confounded Offline Data. ICML Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2025	J	`jnl`	Automatic Reward Shaping from Confounded Offline Data. CoRR Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2025	A*	`conf`	Causal Abstraction Inference under Lossy Representations. ICML Kevin Muyuan Xia, Elias Bareinboim
2025	J	`jnl`	Causal Abstraction Inference under Lossy Representations. CoRR Kevin Xia, Elias Bareinboim
2025	A	`conf`	Causal Eligibility Traces for Confounding Robust Off-Policy Evaluation. UAI Junzhe Zhang, Elias Bareinboim
2025	J	`jnl`	Causally Aligned Curriculum Learning. CoRR Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2025	J	`jnl`	Confounding Robust Deep Reinforcement Learning: A Causal Approach. CoRR Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2025	A*	`conf`	Counterfactual Graphical Models: Constraints and Inference. ICML Juan D. Correa, Elias Bareinboim
2025	A*	`conf`	Counterfactual Identification Under Monotonicity Constraints. AAAI Aurghya Maiti, Drago Plecko, Elias Bareinboim
2025	A*	`conf`	Counterfactual Realizability. ICLR Arvind Raghavan, Elias Bareinboim
2025	J	`jnl`	Counterfactual Realizability. CoRR Arvind Raghavan, Elias Bareinboim
2025	J	`jnl`	Epidemiology of Large Language Models: A Benchmark for Observational Distribution Knowledge. CoRR Drago Plecko, Patrik Okanovic, Torsten Hoefler, Elias Bareinboim
2025	A*	`conf`	Fairness-Accuracy Trade-Offs: A Causal Perspective. AAAI Drago Plecko, Elias Bareinboim
2025	J	`jnl`	From Black-box to Causal-box: Towards Building More Interpretable Models. CoRR Inwoo Hwang, Yushu Pan, Elias Bareinboim
2025	J	`jnl`	Less Greedy Equivalence Search. CoRR Adiba Ejaz, Elias Bareinboim
2025	J	`jnl`	Partial Identification Approach to Counterfactual Fairness Assessment. CoRR Saeyoung Rho, Junzhe Zhang, Elias Bareinboim
2025	J	`jnl`	Partial Transportability for Domain Generalization. CoRR Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim
2025	A*	`conf`	Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. AAAI Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
2024	J	`jnl`	Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. Found. Trends Mach. Learn. Drago Plecko, Elias Bareinboim
2024	A*	`conf`	Causal Imitation for Markov Decision Processes: a Partial Identification Approach. NeurIPS Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim
2024	A*	`conf`	Causally Aligned Curriculum Learning. ICLR Mingxuan Li, Junzhe Zhang, Elias Bareinboim
2024	A*	`conf`	Counterfactual Image Editing. ICML Yushu Pan, Elias Bareinboim
2024	J	`jnl`	Counterfactual Image Editing. CoRR Yushu Pan, Elias Bareinboim
2024	A*	`conf`	Disentangled Representation Learning in Non-Markovian Causal Systems. NeurIPS Adam Li, Yushu Pan, Elias Bareinboim
2024	J	`jnl`	Fairness-Accuracy Trade-Offs: A Causal Perspective. CoRR Drago Plecko, Elias Bareinboim
2024	A*	`conf`	Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making. NeurIPS Drago Plecko, Elias Bareinboim
2024	J	`jnl`	Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making. CoRR Drago Plecko, Elias Bareinboim
2024	A*	`conf`	Neural Causal Abstractions. AAAI Kevin Xia, Elias Bareinboim
2024	J	`jnl`	Neural Causal Abstractions. CoRR Kevin Xia, Elias Bareinboim
2024	A*	`conf`	Partial Transportability for Domain Generalization. NeurIPS Kasra Jalaldoust, Alexis Bellot, Elias Bareinboim
2024	A*	`conf`	Reconciling Predictive and Statistical Parity: A Causal Approach. AAAI Drago Plecko, Elias Bareinboim
2024	A*	`conf`	Scores for Learning Discrete Causal Graphs with Unobserved Confounders. AAAI Alexis Bellot, Junzhe Zhang, Elias Bareinboim
2024	J	`jnl`	Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. CoRR Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim
2024	A*	`conf`	Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. AAAI Shalmali Joshi, Junzhe Zhang, Elias Bareinboim
2024	A*	`conf`	Transportable Representations for Domain Generalization. AAAI Kasra Jalaldoust, Elias Bareinboim
2024	A*	`conf`	Unified Covariate Adjustment for Causal Inference. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2023	A*	`conf`	A Causal Framework for Decomposing Spurious Variations. NeurIPS Drago Plecko, Elias Bareinboim
2023	J	`jnl`	A Causal Framework for Decomposing Spurious Variations. CoRR Drago Plecko, Elias Bareinboim
2023	A*	`conf`	Causal Effect Identification in Cluster DAGs. AAAI Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
2023	A*	`conf`	Causal Fairness for Outcome Control. NeurIPS Drago Plecko, Elias Bareinboim
2023	J	`jnl`	Causal Fairness for Outcome Control. CoRR Drago Plecko, Elias Bareinboim
2023	A*	`conf`	Causal Imitation Learning via Inverse Reinforcement Learning. ICLR Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim
2023	A*	`conf`	Causal discovery from observational and interventional data across multiple environments. NeurIPS Adam Li, Amin Jaber, Elias Bareinboim
2023	J	`jnl`	Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory Negar Kiyavash, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak
2023	A*	`conf`	Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. NeurIPS Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim
2023	A*	`conf`	Estimating Joint Treatment Effects by Combining Multiple Experiments. ICML Yonghan Jung, Jin Tian, Elias Bareinboim
2023	A*	`conf`	Neural Causal Models for Counterfactual Identification and Estimation. ICLR Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim
2023	A*	`conf`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	J	`jnl`	Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf
2023	J	`jnl`	Reconciling Predictive and Statistical Parity: A Causal Approach. CoRR Drago Plecko, Elias Bareinboim
2022	—	`conf`	Can Humans Be out of the Loop? CLeaR Junzhe Zhang, Elias Bareinboim
2022	J	`jnl`	Causal Fairness Analysis. CoRR Drago Plecko, Elias Bareinboim
2022	A*	`conf`	Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. NeurIPS Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim
2022	J	`jnl`	Causal Imitation Learning with Unobserved Confounders. CoRR Junzhe Zhang, Daniel Kumor, Elias Bareinboim
2022	A*	`conf`	Causal Transportability for Visual Recognition. CVPR Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick
2022	J	`jnl`	Causal Transportability for Visual Recognition. CoRR Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick
2022	A*	`conf`	Counterfactual Transportability: A Formal Approach. ICML Juan D. Correa, Sanghack Lee, Elias Bareinboim
2022	J	`jnl`	Effect Identification in Cluster Causal Diagrams. CoRR Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim
2022	—	`ch.`	External Validity: From Probabilistic and Causal Inference Judea Pearl, Elias Bareinboim
2022	A*	`conf`	Finding and Listing Front-door Adjustment Sets. NeurIPS Hyunchai Jeong, Jin Tian, Elias Bareinboim
2022	J	`jnl`	Finding and Listing Front-door Adjustment Sets. CoRR Hyunchai Jeong, Jin Tian, Elias Bareinboim
2022	J	`jnl`	Neural Causal Models for Counterfactual Identification and Estimation. CoRR Kevin Xia, Yushu Pan, Elias Bareinboim
2022	A*	`conf`	On Measuring Causal Contributions via do-interventions. ICML Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim
2022	—	`ch.`	On Pearl's Hierarchy and the Foundations of Causal Inference. Probabilistic and Causal Inference Elias Bareinboim, Juan D. Correa, Duligur Ibeling, Thomas Icard
2022	A*	`conf`	Online Reinforcement Learning for Mixed Policy Scopes. NeurIPS Junzhe Zhang, Elias Bareinboim
2022	A*	`conf`	Partial Counterfactual Identification from Observational and Experimental Data. ICML Junzhe Zhang, Jin Tian, Elias Bareinboim
2022	—	`ch.`	Recovering from Selection Bias in Causal and Statistical Inference. Probabilistic and Causal Inference Elias Bareinboim, Jin Tian, Judea Pearl
2022	J	`jnl`	Sequential Causal Imitation Learning with Unobserved Confounders. CoRR Daniel Kumor, Junzhe Zhang, Elias Bareinboim
2021	A*	`conf`	Bounding Causal Effects on Continuous Outcome. AAAI Junzhe Zhang, Elias Bareinboim
2021	A*	`conf`	Causal Identification with Matrix Equations. NeurIPS Sanghack Lee, Elias Bareinboim
2021	A*	`conf`	Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2021	A*	`conf`	Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. ICML Yonghan Jung, Jin Tian, Elias Bareinboim
2021	A*	`conf`	Estimating Identifiable Causal Effects through Double Machine Learning. AAAI Yonghan Jung, Jin Tian, Elias Bareinboim
2021	A*	`conf`	Nested Counterfactual Identification from Arbitrary Surrogate Experiments. NeurIPS Juan D. Correa, Sanghack Lee, Elias Bareinboim
2021	J	`jnl`	Nested Counterfactual Identification from Arbitrary Surrogate Experiments. CoRR Juan D. Correa, Sanghack Lee, Elias Bareinboim
2021	J	`jnl`	Partial Counterfactual Identification from Observational and Experimental Data. CoRR Junzhe Zhang, Jin Tian, Elias Bareinboim
2021	—	`conf`	Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. CD@KDD Thuc Duy Le, Jiuyong Li, Gregory F. Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash
2021	A*	`conf`	Sequential Causal Imitation Learning with Unobserved Confounders. NeurIPS Daniel Kumor, Junzhe Zhang, Elias Bareinboim
2021	A*	`conf`	The Causal-Neural Connection: Expressiveness, Learnability, and Inference. NeurIPS Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
2021	J	`jnl`	The Causal-Neural Connection: Expressiveness, Learnability, and Inference. CoRR Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
2021	A*	`conf`	The KDD 2021 Workshop on Causal Discovery (CD2021). KDD Thuc Duy Le, Jiuyong Li, Gregory F. Cooper, Sofia Triantafillou, Elias Bareinboim, Huan Liu, Negar Kiyavash
2021	—	`ed.`	The KDD 2021 Workshop on Causal Discovery, CD@KDD 2021, Singapore, August 15, 2021. CD@KDD Thuc Duy Le, Jiuyong Li, Gregory F. Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash
2020	A*	`conf`	A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments. AAAI Juan D. Correa, Elias Bareinboim
2020	A*	`conf`	Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. NeurIPS Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
2020	A*	`conf`	Causal Effect Identifiability under Partial-Observability. ICML Sanghack Lee, Elias Bareinboim
2020	A*	`conf`	Causal Imitation Learning With Unobserved Confounders. NeurIPS Junzhe Zhang, Daniel Kumor, Elias Bareinboim
2020	Misc	`conf`	Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang
2020	A*	`conf`	Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. NeurIPS Sanghack Lee, Elias Bareinboim
2020	A*	`conf`	Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. ICML Daniel Kumor, Carlos Cinelli, Elias Bareinboim
2020	A*	`conf`	Estimating Causal Effects Using Weighting-Based Estimators. AAAI Yonghan Jung, Jin Tian, Elias Bareinboim
2020	A*	`conf`	General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. AAAI Sanghack Lee, Juan D. Correa, Elias Bareinboim
2020	A*	`conf`	General Transportability of Soft Interventions: Completeness Results. NeurIPS Juan D. Correa, Elias Bareinboim
2020	A*	`conf`	Identifiability from a Combination of Observations and Experiments. AAAI Sanghack Lee, Juan D. Correa, Elias Bareinboim
2020	A*	`conf`	Learning Causal Effects via Weighted Empirical Risk Minimization. NeurIPS Yonghan Jung, Jin Tian, Elias Bareinboim
2019	A*	`conf`	Adjustment Criteria for Generalizing Experimental Findings. ICML Juan D. Correa, Jin Tian, Elias Bareinboim
2019	A*	`conf`	Causal Identification under Markov Equivalence: Completeness Results. ICML Amin Jaber, Jiji Zhang, Elias Bareinboim
2019	A*	`conf`	Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. NeurIPS Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim
2019	A*	`conf`	Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. AAAI Andrew Forney, Elias Bareinboim
2019	A*	`conf`	Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. NeurIPS Daniel Kumor, Bryant Chen, Elias Bareinboim
2019	J	`jnl`	Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. CoRR Daniel Kumor, Bryant Chen, Elias Bareinboim
2019	A*	`conf`	From Statistical Transportability to Estimating the Effect of Stochastic Interventions. IJCAI Juan D. Correa, Elias Bareinboim
2019	A	`conf`	General Identifiability with Arbitrary Surrogate Experiments. UAI Sanghack Lee, Juan D. Correa, Elias Bareinboim
2019	A*	`conf`	Identification of Causal Effects in the Presence of Selection Bias. AAAI Juan D. Correa, Jin Tian, Elias Bareinboim
2019	A*	`conf`	Identification of Conditional Causal Effects under Markov Equivalence. NeurIPS Amin Jaber, Jiji Zhang, Elias Bareinboim
2019	A*	`conf`	Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. NeurIPS Junzhe Zhang, Elias Bareinboim
2019	A*	`conf`	On Causal Identification under Markov Equivalence. IJCAI Amin Jaber, Jiji Zhang, Elias Bareinboim
2019	A*	`conf`	Sensitivity Analysis of Linear Structural Causal Models. ICML Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim
2019	A*	`conf`	Structural Causal Bandits with Non-Manipulable Variables. AAAI Sanghack Lee, Elias Bareinboim
2018	A*	`conf`	A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams. IJCAI Amin Jaber, Jiji Zhang, Elias Bareinboim
2018	A*	`conf`	Budgeted Experiment Design for Causal Structure Learning. ICML AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
2018	A	`conf`	Causal Identification under Markov Equivalence. UAI Amin Jaber, Jiji Zhang, Elias Bareinboim
2018	J	`jnl`	Causal Identification under Markov Equivalence. CoRR Amin Jaber, Jiji Zhang, Elias Bareinboim
2018	A	`conf`	Characterizing the Limits of Autonomous Systems. AAMAS Junzhe Zhang, Elias Bareinboim
2018	A*	`conf`	Equality of Opportunity in Classification: A Causal Approach. NeurIPS Junzhe Zhang, Elias Bareinboim
2018	A*	`conf`	Fairness in Decision-Making - The Causal Explanation Formula. AAAI Junzhe Zhang, Elias Bareinboim
2018	A*	`conf`	Generalized Adjustment Under Confounding and Selection Biases. AAAI Juan D. Correa, Jin Tian, Elias Bareinboim
2018	A	`conf`	Non-Parametric Path Analysis in Structural Causal Models. UAI Junzhe Zhang, Elias Bareinboim
2018	A*	`conf`	Structural Causal Bandits: Where to Intervene? NeurIPS Sanghack Lee, Elias Bareinboim
2017	J	`jnl`	Budgeted Experiment Design for Causal Structure Learning. CoRR AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim
2017	A*	`conf`	Causal Effect Identification by Adjustment under Confounding and Selection Biases. AAAI Juan D. Correa, Elias Bareinboim
2017	A*	`conf`	Counterfactual Data-Fusion for Online Reinforcement Learners. ICML Andrew Forney, Judea Pearl, Elias Bareinboim
2017	—	`conf`	Experimental Design for Learning Causal Graphs with Latent Variables. NIPS Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim
2017	J	`jnl`	Guest editorial: special issue on causal discovery. Int. J. Data Sci. Anal. Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu
2017	A*	`conf`	Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. ICML Bryant Chen, Daniel Kumor, Elias Bareinboim
2017	—	`ed.`	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. CFA@UAI Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva
2017	A	`conf`	Transfer Learning in Multi-Armed Bandit: A Causal Approach. AAMAS Junzhe Zhang, Elias Bareinboim
2017	A*	`conf`	Transfer Learning in Multi-Armed Bandits: A Causal Approach. IJCAI Junzhe Zhang, Elias Bareinboim
2016	J	`jnl`	AI's 10 to Watch. IEEE Intell. Syst. Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh
2016	J	`jnl`	Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. USA Elias Bareinboim, Judea Pearl
2016	A*	`conf`	Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables. IJCAI Bryant Chen, Judea Pearl, Elias Bareinboim
2016	J	`jnl`	Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl
2015	—	`conf`	Bandits with Unobserved Confounders: A Causal Approach. NIPS Elias Bareinboim, Andrew Forney, Judea Pearl
2015	J	`jnl`	External Validity: From Do-Calculus to Transportability Across Populations. CoRR Judea Pearl, Elias Bareinboim
2015	J	`jnl`	Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models. CoRR Bryant Chen, Judea Pearl, Elias Bareinboim
2015	A*	`conf`	Recovering Causal Effects from Selection Bias. AAAI Elias Bareinboim, Jin Tian
2014	—	`—`	Generalizability in Causal Inference: Theory and Algorithms. Elias Bareinboim
2014	J	`jnl`	Generalizing causal knowledge: theory and algorithms. AI Matters Elias Bareinboim, Judea Pearl
2014	A*	`conf`	Recovering from Selection Bias in Causal and Statistical Inference. AAAI Elias Bareinboim, Jin Tian, Judea Pearl
2014	—	`conf`	Transportability from Multiple Environments with Limited Experiments: Completeness Results. NIPS Elias Bareinboim, Judea Pearl
2013	J	`jnl`	A General Algorithm for Deciding Transportability of Experimental Results. CoRR Elias Bareinboim, Judea Pearl
2013	A*	`conf`	Causal Transportability with Limited Experiments. AAAI Elias Bareinboim, Judea Pearl
2013	A	`conf`	Meta-Transportability of Causal Effects: A Formal Approach. AISTATS Elias Bareinboim, Judea Pearl
2013	—	`conf`	Transportability from Multiple Environments with Limited Experiments. NIPS Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl
2012	J	`jnl`	Causal Inference by Surrogate Experiments: z-Identifiability CoRR Elias Bareinboim, Judea Pearl
2012	A	`conf`	Causal Inference by Surrogate Experiments: z-Identifiability. UAI Elias Bareinboim, Judea Pearl
2012	A	`conf`	Controlling Selection Bias in Causal Inference. AISTATS Elias Bareinboim, Judea Pearl
2012	A*	`conf`	Transportability of Causal Effects: Completeness Results. AAAI Elias Bareinboim, Judea Pearl
2011	J	`jnl`	Analyzing marginal cases in differential shotgun proteomics. Bioinform. Paulo C. Carvalho, Juliana S. G. Fischer, Jonas Perales, John R. Yates III, Valmir Carneiro Barbosa, Elias Bareinboim
2011	A*	`conf`	Controlling Selection Bias in Causal Inference. AAAI Elias Bareinboim, Judea Pearl
2011	—	`conf`	Local Characterizations of Causal Bayesian Networks. GKR Elias Bareinboim, Carlos Brito, Judea Pearl
2011	A*	`conf`	Transportability of Causal and Statistical Relations: A Formal Approach. AAAI Judea Pearl, Elias Bareinboim
2011	—	`conf`	Transportability of Causal and Statistical Relations: A Formal Approach. ICDM Workshops Judea Pearl, Elias Bareinboim