Elias Bareinboim

发表

Generalizability in Causal Inference: Theory and Algorithms

Elias Bareinboim, E. Bareinboim, 2014 .

Identifiability from a Combination of Observations and Experiments

Elias Bareinboim, Sanghack Lee, Juan D. Correa, 2020, AAAI.

Generalized Adjustment Under Confounding and Selection Biases

Jin Tian, Elias Bareinboim, Juan D. Correa, 2018, AAAI.

Estimating Causal Effects Using Weighting-Based Estimators

Elias Bareinboim, Jin Tian, Yonghan Jung, 2020, AAAI.

Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets

Elias Bareinboim, Carlos Cinelli, Daniel Kumor, 2020, ICML.

Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables

Elias Bareinboim, Daniel Kumor, Bryant Chen, 2016, ICML.

Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets

Elias Bareinboim, Daniel Kumor, Bryant Chen, 2019, NeurIPS.

Causal Inference from Big Data: Theoretical Foundations and the Data-fusion Problem

Elias Bareinboim, Judea Pearl, J. Pearl, 2015 .

Causal Effect Identification by Adjustment under Confounding and Selection Biases

Elias Bareinboim, Juan D. Correa, E. Bareinboim, 2017, AAAI.

Learning Causal Effects via Weighted Empirical Risk Minimization

Jin Tian, Elias Bareinboim, Yonghan Jung, 2020, NeurIPS.

General Identifiability with Arbitrary Surrogate Experiments

Elias Bareinboim, Sanghack Lee, Juan D. Correa, 2019, UAI.

External Validity: From Do-Calculus to Transportability Across Populations pdf

Elias Bareinboim, Judea Pearl, J. Pearl, 2014, Probabilistic and Causal Inference.

Bioinformatics Applications Note Analyzing Marginal Cases in Differential Shotgun Proteomics

Elias Bareinboim, Valmir Carneiro Barbosa, John R. Yates, 2022 .

Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions

Karthikeyan Shanmugam, Murat Kocaoglu, Elias Bareinboim, 2019, NeurIPS.

Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models pdf

Elias Bareinboim, Judea Pearl, Bryant Chen, 2015, ArXiv.

Causal Inference and Data Fusion in Econometrics

Elias Bareinboim, Paul Hunermund, E. Bareinboim, 2019, The Econometrics Journal.

Guest editorial: special issue on causal discovery

Jiuyong Li, Lin Liu, Kun Zhang, 2016, International Journal of Data Science and Analytics.

A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams

Jiji Zhang, Elias Bareinboim, Amin Jaber, 2018, IJCAI.

Causal inference and the data-fusion problem

Elias Bareinboim, Judea Pearl, J. Pearl, 2016, Proceedings of the National Academy of Sciences.

Generalizing causal knowledge: theory and algorithms

Elias Bareinboim, Judea Pearl, J. Pearl, 2014, SIGAI.

Transportability of Causal Effects: Completeness Results

Elias Bareinboim, Judea Pearl, J. Pearl, 2012, AAAI.

Sensitivity Analysis of Linear Structural Causal Models

Elias Bareinboim, Judea Pearl, Carlos Cinelli, 2019, ICML.

Identification of Conditional Causal Effects under Markov Equivalence

Jiji Zhang, Elias Bareinboim, Amin Jaber, 2019, NeurIPS.

Causal Effect Identifiability under Partial-Observability

Elias Bareinboim, Sanghack Lee, E. Bareinboim, 2020, ICML.

Causal Identification under Markov Equivalence

Jiji Zhang, Elias Bareinboim, Amin Jaber, 2018, UAI.

Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe

Elias Bareinboim, Sanghack Lee, E. Bareinboim, 2020, NeurIPS.

Transfer Learning in Multi-Armed Bandit: A Causal Approach

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2017, AAMAS.

Recovering from Selection Bias in Causal and Statistical Inference

Jin Tian, Elias Bareinboim, Judea Pearl, 2014, AAAI.

Structural Causal Bandits: Where to Intervene?

Elias Bareinboim, Sanghack Lee, E. Bareinboim, 2018, NeurIPS.

General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains

Elias Bareinboim, Sanghack Lee, Juan D. Correa, 2020, AAAI.

A General Algorithm for Deciding Transportability of Experimental Results

Elias Bareinboim, Judea Pearl, J. Pearl, 2013, ArXiv.

Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2019, NeurIPS.

Experimental Design for Learning Causal Graphs with Latent Variables

Karthikeyan Shanmugam, Murat Kocaoglu, Elias Bareinboim, 2017, NIPS.

Equality of Opportunity in Classification: A Causal Approach

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2018, NeurIPS.

Identification of Causal Effects in the Presence of Selection Bias

Jin Tian, Elias Bareinboim, Juan D. Correa, 2019, AAAI.

Bandits with Unobserved Confounders: A Causal Approach

Elias Bareinboim, Judea Pearl, Andrew Forney, 2015, NIPS.

Controlling Selection Bias in Causal Inference

Elias Bareinboim, Judea Pearl, J. Pearl, 2011, AISTATS.

Recovering Causal Effects from Selection Bias

Jin Tian, Elias Bareinboim, E. Bareinboim, 2015, AAAI.

Counterfactual Data-Fusion for Online Reinforcement Learners

Elias Bareinboim, Judea Pearl, Andrew Forney, 2017, ICML.

Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables

Elias Bareinboim, Judea Pearl, Bryant Chen, 2015, IJCAI.

Inferência gramatical aplicada à modelagem lingüística de redes de regulação biológicas

Elias Bareinboim, João Carlos Pereira da Silva, Ana Tereza Ribeiro Vasconcelos, 2007 .

Transportability of Causal and Statistical Relations: A Formal Approach

Elias Bareinboim, Judea Pearl, J. Pearl, 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

Meta-Transportability of Causal Effects: A Formal Approach

Elias Bareinboim, Judea Pearl, J. Pearl, 2013, AISTATS.

Local Characterizations of Causal Bayesian Networks

Elias Bareinboim, Judea Pearl, Carlos Brito, 2011, GKR.

Transportability across studies: A formal approach

Elias Bareinboim, Judea Pearl, J. Pearl, 2011 .

Transportability from Multiple Environments with Limited Experiments: Completeness Results

Elias Bareinboim, Judea Pearl, J. Pearl, 2014, NIPS.

General Transportability of Soft Interventions: Completeness Results

Elias Bareinboim, Juan D. Correa, E. Bareinboim, 2020, NeurIPS.

Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding

Elias Bareinboim, Andrew Forney, E. Bareinboim, 2019, AAAI.

Causal Identification under Markov Equivalence: Completeness Results

Jiji Zhang, Elias Bareinboim, Amin Jaber, 2019, ICML.

Structural Causal Bandits with Non-Manipulable Variables

Elias Bareinboim, Sanghack Lee, E. Bareinboim, 2019, AAAI.

Transportability from Multiple Environments with Limited Experiments

Vasant Honavar, Elias Bareinboim, Judea Pearl, 2013, NIPS.

Budgeted Experiment Design for Causal Structure Learning

Elias Bareinboim, AmirEmad Ghassami, Saber Salehkaleybar, 2017, ICML.

Characterizing the Limits of Autonomous Systems

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2018, AAMAS.

A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments

Elias Bareinboim, Juan C. Correa, J. C. Correa, 2020, AAAI.

Preface to the ACM TIST Special Issue on Causal Discovery and Inference

Bernhard Schölkopf, Jiuyong Li, Kun Zhang, 2016, ACM Trans. Intell. Syst. Technol..

From Statistical Transportability to Estimating the Effect of Stochastic Interventions

Elias Bareinboim, Juan D. Correa, E. Bareinboim, 2019, IJCAI.

Fairness in Decision-Making - The Causal Explanation Formula

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2018, AAAI.

Causal Imitation Learning With Unobserved Confounders

Elias Bareinboim, Daniel Kumor, Junzhe Zhang, 2022, NeurIPS.

Causal Transportability with Limited Experiments

Elias Bareinboim, Judea Pearl, J. Pearl, 2013, AAAI.

Causal Inference by Surrogate Experiments: z-Identifiability

Elias Bareinboim, Judea Pearl, J. Pearl, 2012, UAI.

Non-Parametric Path Analysis in Structural Causal Models

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2018, UAI.

Adjustment Criteria for Generalizing Experimental Findings

Jin Tian, Elias Bareinboim, Juan D. Correa, 2019, ICML.

The Causal-Neural Connection: Expressiveness, Learnability, and Inference pdf

Yoshua Bengio, Elias Bareinboim, Kai-Zhan Lee, 2021, ArXiv.

Nested Counterfactual Identification from Arbitrary Surrogate Experiments pdf

Elias Bareinboim, Juan D Correa, Sanghack Lee, 2021, NeurIPS.

Estimating Identifiable Causal Effects through Double Machine Learning

Jin Tian, Elias Bareinboim, Yonghan Jung, 2021, AAAI.

The KDD 2021 Workshop on Causal Discovery (CD2021)

Elias Bareinboim, Negar Kiyavash, Sofia Triantafillou, 2021, KDD.

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

Jin Tian, Elias Bareinboim, Yonghan Jung, 2021, ICML.

Partial Counterfactual Identification from Observational and Experimental Data pdf

Elias Bareinboim, Jin Tian, Junzhe Zhang, 2021, ICML.

Bounding Causal Effects on Continuous Outcome

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2021, AAAI.

Descents and nodal load in scale-free networks.

Elias Bareinboim, E. Bareinboim, V. Barbosa, 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

Markov Decision Processes with Unobserved Confounders : A Causal Approach

Elias Bareinboim, Junzhe Zhang, E. Bareinboim, 2016 .

Grammatical inference applied to linguistic modeling of biological regulation networks

Elias Bareinboim, João Carlos Pereira da Silva, E. Bareinboim, 2007 .

Controlling Selection Bias in Causal Inference ( Supplementary Material )

Elias Bareinboim, Judea Pearl, 2012 .

Learning Causal Graphs with Latent Variables

Karthikeyan Shanmugam, Murat Kocaoglu, Elias Bareinboim, 2017, NIPS 2017.