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2020 – today
- 2024
[j8]Drago Plecko, Elias Bareinboim:
Causal Fairness Analysis: A Causal Toolkit for Fair Machine Learning. Found. Trends Mach. Learn. 17(3): 304-589 (2024)- [c89]Alexis Bellot, Junzhe Zhang, Elias Bareinboim:
Scores for Learning Discrete Causal Graphs with Unobserved Confounders. AAAI 2024: 11043-11051 - [c88]Kasra Jalaldoust, Elias Bareinboim:
Transportable Representations for Domain Generalization. AAAI 2024: 12790-12800 - [c87]Shalmali Joshi, Junzhe Zhang, Elias Bareinboim:
Towards Safe Policy Learning under Partial Identifiability: A Causal Approach. AAAI 2024: 13004-13012 - [c86]Drago Plecko, Elias Bareinboim:
Reconciling Predictive and Statistical Parity: A Causal Approach. AAAI 2024: 14625-14632 - [c85]Kevin Xia, Elias Bareinboim:
Neural Causal Abstractions. AAAI 2024: 20585-20595 - [c84]Mingxuan Li, Junzhe Zhang, Elias Bareinboim:
Causally Aligned Curriculum Learning. ICLR 2024 - [c83]Yushu Pan, Elias Bareinboim:
Counterfactual Image Editing. ICML 2024 - [i26]Kevin Xia, Elias Bareinboim:
Neural Causal Abstractions. CoRR abs/2401.02602 (2024) - [i25]Yushu Pan, Elias Bareinboim:
Counterfactual Image Editing. CoRR abs/2403.09683 (2024) - [i24]Drago Plecko, Elias Bareinboim:
Fairness-Accuracy Trade-Offs: A Causal Perspective. CoRR abs/2405.15443 (2024) - [i23]Drago Plecko, Elias Bareinboim:
Mind the Gap: A Causal Perspective on Bias Amplification in Prediction & Decision-Making. CoRR abs/2405.15446 (2024) - [i22]Hyunchai Jeong, Adiba Ejaz, Jin Tian, Elias Bareinboim:
Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies. CoRR abs/2409.14593 (2024) - 2023
- [j7]Negar Kiyavash
, Elias Bareinboim, Todd P. Coleman, Alex Dimakis, Bernhard Schlkopf, Peter Spirtes, Kun Zhang, Robert Nowak:
Editorial Special Issue on Causality: Fundamental Limits and Applications. IEEE J. Sel. Areas Inf. Theory 4: iv (2023) - [c82]Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim:
Causal Effect Identification in Cluster DAGs. AAAI 2023: 12172-12179 - [c81]Kangrui Ruan, Junzhe Zhang, Xuan Di, Elias Bareinboim:
Causal Imitation Learning via Inverse Reinforcement Learning. ICLR 2023 - [c80]Kevin Muyuan Xia, Yushu Pan, Elias Bareinboim:
Neural Causal Models for Counterfactual Identification and Estimation. ICLR 2023 - [c79]Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Joint Treatment Effects by Combining Multiple Experiments. ICML 2023: 15451-15527 - [c78]Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim:
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. NeurIPS 2023 - [c77]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS 2023 - [c76]Adam Li, Amin Jaber, Elias Bareinboim:
Causal discovery from observational and interventional data across multiple environments. NeurIPS 2023 - [c75]Drago Plecko, Elias Bareinboim:
A Causal Framework for Decomposing Spurious Variations. NeurIPS 2023 - [c74]Drago Plecko, Elias Bareinboim:
Causal Fairness for Outcome Control. NeurIPS 2023 - [i21]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i20]Drago Plecko, Elias Bareinboim:
Reconciling Predictive and Statistical Parity: A Causal Approach. CoRR abs/2306.05059 (2023) - [i19]Drago Plecko, Elias Bareinboim:
Causal Fairness for Outcome Control. CoRR abs/2306.05066 (2023) - [i18]Drago Plecko, Elias Bareinboim:
A Causal Framework for Decomposing Spurious Variations. CoRR abs/2306.05071 (2023) - 2022
- [c73]Junzhe Zhang, Elias Bareinboim:
Can Humans Be out of the Loop? CLeaR 2022: 1010-1025 - [c72]Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick:
Causal Transportability for Visual Recognition. CVPR 2022: 7511-7521 - [c71]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Counterfactual Transportability: A Formal Approach. ICML 2022: 4370-4390 - [c70]Yonghan Jung, Shiva Prasad Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Blöbaum, Elias Bareinboim:
On Measuring Causal Contributions via do-interventions. ICML 2022: 10476-10501 - [c69]Junzhe Zhang, Jin Tian, Elias Bareinboim:
Partial Counterfactual Identification from Observational and Experimental Data. ICML 2022: 26548-26558 - [c68]Amin Jaber, Adèle H. Ribeiro, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. NeurIPS 2022 - [c67]Hyunchai Jeong, Jin Tian, Elias Bareinboim:
Finding and Listing Front-door Adjustment Sets. NeurIPS 2022 - [c66]Junzhe Zhang, Elias Bareinboim:
Online Reinforcement Learning for Mixed Policy Scopes. NeurIPS 2022 - [p3]Elias Bareinboim, Jin Tian, Judea Pearl:
Recovering from Selection Bias in Causal and Statistical Inference. Probabilistic and Causal Inference 2022: 433-450 - [p2]Judea Pearl, Elias Bareinboim:
External Validity: From Do-Calculus to Transportability Across Populations. Probabilistic and Causal Inference 2022: 451-482 - [p1]Elias Bareinboim, Juan D. Correa, Duligur Ibeling, Thomas Icard:
On Pearl's Hierarchy and the Foundations of Causal Inference. Probabilistic and Causal Inference 2022: 507-556 - [i17]Tara V. Anand, Adèle H. Ribeiro, Jin Tian, Elias Bareinboim:
Effect Identification in Cluster Causal Diagrams. CoRR abs/2202.12263 (2022) - [i16]Chengzhi Mao, Kevin Xia, James Wang, Hao Wang, Junfeng Yang, Elias Bareinboim, Carl Vondrick:
Causal Transportability for Visual Recognition. CoRR abs/2204.12363 (2022) - [i15]Drago Plecko, Elias Bareinboim:
Causal Fairness Analysis. CoRR abs/2207.11385 (2022) - [i14]Junzhe Zhang, Daniel Kumor, Elias Bareinboim:
Causal Imitation Learning with Unobserved Confounders. CoRR abs/2208.06267 (2022) - [i13]Daniel Kumor, Junzhe Zhang, Elias Bareinboim:
Sequential Causal Imitation Learning with Unobserved Confounders. CoRR abs/2208.06276 (2022) - [i12]Kevin Xia, Yushu Pan, Elias Bareinboim:
Neural Causal Models for Counterfactual Identification and Estimation. CoRR abs/2210.00035 (2022) - [i11]Hyunchai Jeong, Jin Tian, Elias Bareinboim:
Finding and Listing Front-door Adjustment Sets. CoRR abs/2210.05816 (2022) - 2021
- [c65]Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Identifiable Causal Effects through Double Machine Learning. AAAI 2021: 12113-12122 - [c64]Junzhe Zhang, Elias Bareinboim:
Bounding Causal Effects on Continuous Outcome. AAAI 2021: 12207-12215 - [c63]Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. ICML 2021: 5168-5179 - [c62]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
Preface: The 2021 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2021: 1-2 - [c61]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafillou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
The KDD 2021 Workshop on Causal Discovery (CD2021). KDD 2021: 4141-4142 - [c60]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. NeurIPS 2021: 6856-6867 - [c59]Sanghack Lee, Elias Bareinboim:
Causal Identification with Matrix Equations. NeurIPS 2021: 9468-9479 - [c58]Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim:
The Causal-Neural Connection: Expressiveness, Learnability, and Inference. NeurIPS 2021: 10823-10836 - [c57]Daniel Kumor, Junzhe Zhang, Elias Bareinboim:
Sequential Causal Imitation Learning with Unobserved Confounders. NeurIPS 2021: 14669-14680 - [c56]Yonghan Jung, Jin Tian, Elias Bareinboim:
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. NeurIPS 2021: 21821-21833 - [e2]Thuc Duy Le, Jiuyong Li, Gregory Cooper, Sofia Triantafyllou, Elias Bareinboim, Huan Liu, Negar Kiyavash:
The KDD 2021 Workshop on Causal Discovery, CD@KDD 2021, Singapore, August 15, 2021. Proceedings of Machine Learning Research 150, PMLR 2021 [contents] - [i10]Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim:
The Causal-Neural Connection: Expressiveness, Learnability, and Inference. CoRR abs/2107.00793 (2021) - [i9]Juan D. Correa, Sanghack Lee, Elias Bareinboim:
Nested Counterfactual Identification from Arbitrary Surrogate Experiments. CoRR abs/2107.03190 (2021) - [i8]Junzhe Zhang, Jin Tian, Elias Bareinboim:
Partial Counterfactual Identification from Observational and Experimental Data. CoRR abs/2110.05690 (2021) - 2020
- [c55]Juan D. Correa, Elias Bareinboim:
A Calculus for Stochastic Interventions: Causal Effect Identification and Surrogate Experiments. AAAI 2020: 10093-10100 - [c54]Yonghan Jung, Jin Tian, Elias Bareinboim:
Estimating Causal Effects Using Weighting-Based Estimators. AAAI 2020: 10186-10193 - [c53]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. AAAI 2020: 10210-10217 - [c52]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
Identifiability from a Combination of Observations and Experiments. AAAI 2020: 13677-13680 - [c51]George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang:
Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA 2020 - [c50]Daniel Kumor, Carlos Cinelli, Elias Bareinboim:
Efficient Identification in Linear Structural Causal Models with Auxiliary Cutsets. ICML 2020: 5501-5510 - [c49]Sanghack Lee, Elias Bareinboim:
Causal Effect Identifiability under Partial-Observability. ICML 2020: 5692-5701 - [c48]Juan D. Correa, Elias Bareinboim:
General Transportability of Soft Interventions: Completeness Results. NeurIPS 2020 - [c47]Amin Jaber, Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning. NeurIPS 2020 - [c46]Yonghan Jung, Jin Tian, Elias Bareinboim:
Learning Causal Effects via Weighted Empirical Risk Minimization. NeurIPS 2020 - [c45]Sanghack Lee, Elias Bareinboim:
Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe. NeurIPS 2020 - [c44]Junzhe Zhang, Daniel Kumor, Elias Bareinboim:
Causal Imitation Learning With Unobserved Confounders. NeurIPS 2020
2010 – 2019
- 2019
- [c43]Andrew Forney, Elias Bareinboim:
Counterfactual Randomization: Rescuing Experimental Studies from Obscured Confounding. AAAI 2019: 2454-2461 - [c42]Juan D. Correa, Jin Tian, Elias Bareinboim:
Identification of Causal Effects in the Presence of Selection Bias. AAAI 2019: 2744-2751 - [c41]Sanghack Lee, Elias Bareinboim:
Structural Causal Bandits with Non-Manipulable Variables. AAAI 2019: 4164-4172 - [c40]Carlos Cinelli, Daniel Kumor, Bryant Chen, Judea Pearl, Elias Bareinboim:
Sensitivity Analysis of Linear Structural Causal Models. ICML 2019: 1252-1261 - [c39]Juan D. Correa, Jin Tian, Elias Bareinboim:
Adjustment Criteria for Generalizing Experimental Findings. ICML 2019: 1361-1369 - [c38]Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence: Completeness Results. ICML 2019: 2981-2989 - [c37]Juan D. Correa, Elias Bareinboim:
From Statistical Transportability to Estimating the Effect of Stochastic Interventions. IJCAI 2019: 1661-1667 - [c36]Amin Jaber, Jiji Zhang, Elias Bareinboim:
On Causal Identification under Markov Equivalence. IJCAI 2019: 6181-6185 - [c35]Amin Jaber, Jiji Zhang, Elias Bareinboim:
Identification of Conditional Causal Effects under Markov Equivalence. NeurIPS 2019: 11512-11520 - [c34]Daniel Kumor, Bryant Chen, Elias Bareinboim:
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. NeurIPS 2019: 12477-12486 - [c33]Junzhe Zhang, Elias Bareinboim:
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes. NeurIPS 2019: 13401-13411 - [c32]Murat Kocaoglu, Amin Jaber, Karthikeyan Shanmugam, Elias Bareinboim:
Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions. NeurIPS 2019: 14346-14356 - [c31]Sanghack Lee, Juan D. Correa, Elias Bareinboim:
General Identifiability with Arbitrary Surrogate Experiments. UAI 2019: 389-398 - [i7]Daniel Kumor, Bryant Chen, Elias Bareinboim:
Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets. CoRR abs/1910.13493 (2019) - 2018
- [c30]Junzhe Zhang, Elias Bareinboim:
Fairness in Decision-Making - The Causal Explanation Formula. AAAI 2018: 2037-2045 - [c29]Juan D. Correa, Jin Tian, Elias Bareinboim:
Generalized Adjustment Under Confounding and Selection Biases. AAAI 2018: 6335-6342 - [c28]Junzhe Zhang, Elias Bareinboim:
Characterizing the Limits of Autonomous Systems. AAMAS 2018: 2165-2167 - [c27]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim:
Budgeted Experiment Design for Causal Structure Learning. ICML 2018: 1719-1728 - [c26]Amin Jaber, Jiji Zhang, Elias Bareinboim:
A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams. IJCAI 2018: 5024-5030 - [c25]Sanghack Lee, Elias Bareinboim:
Structural Causal Bandits: Where to Intervene? NeurIPS 2018: 2573-2583 - [c24]Junzhe Zhang, Elias Bareinboim:
Equality of Opportunity in Classification: A Causal Approach. NeurIPS 2018: 3675-3685 - [c23]Junzhe Zhang, Elias Bareinboim:
Non-Parametric Path Analysis in Structural Causal Models. UAI 2018: 653-662 - [c22]Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence. UAI 2018: 978-987 - [i6]Amin Jaber, Jiji Zhang, Elias Bareinboim:
Causal Identification under Markov Equivalence. CoRR abs/1812.06209 (2018) - 2017
- [j6]Jiuyong Li, Kun Zhang, Elias Bareinboim, Lin Liu:
Guest editorial: special issue on causal discovery. Int. J. Data Sci. Anal. 3(2): 79-80 (2017) - [c21]Juan D. Correa, Elias Bareinboim:
Causal Effect Identification by Adjustment under Confounding and Selection Biases. AAAI 2017: 3740-3746 - [c20]Junzhe Zhang, Elias Bareinboim:
Transfer Learning in Multi-Armed Bandit: A Causal Approach. AAMAS 2017: 1778-1780 - [c19]Bryant Chen, Daniel Kumor, Elias Bareinboim:
Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables. ICML 2017: 757-766 - [c18]Andrew Forney, Judea Pearl, Elias Bareinboim:
Counterfactual Data-Fusion for Online Reinforcement Learners. ICML 2017: 1156-1164 - [c17]Junzhe Zhang, Elias Bareinboim:
Transfer Learning in Multi-Armed Bandits: A Causal Approach. IJCAI 2017: 1340-1346 - [c16]Murat Kocaoglu, Karthikeyan Shanmugam, Elias Bareinboim:
Experimental Design for Learning Causal Graphs with Latent Variables. NIPS 2017: 7018-7028 - [e1]Frederick Eberhardt, Elias Bareinboim, Marloes H. Maathuis, Joris M. Mooij, Ricardo Silva:
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. CEUR Workshop Proceedings 1792, CEUR-WS.org 2017 [contents] - [i5]AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Elias Bareinboim:
Budgeted Experiment Design for Causal Structure Learning. CoRR abs/1709.03625 (2017) - 2016
- [j5]Haris Aziz, Elias Bareinboim, Yejin Choi, Daniel J. Hsu, Shivaram Kalyanakrishnan, Reshef Meir, Suchi Saria, Gerardo I. Simari, Lirong Xia, William Yeoh:
AI's 10 to Watch. IEEE Intell. Syst. 31(1): 56-66 (2016) - [j4]Elias Bareinboim, Judea Pearl:
Causal inference and the data-fusion problem. Proc. Natl. Acad. Sci. USA 113(27): 7345-7352 (2016) - [j3]Kun Zhang, Jiuyong Li, Elias Bareinboim, Bernhard Schölkopf, Judea Pearl:
Preface to the ACM TIST Special Issue on Causal Discovery and Inference. ACM Trans. Intell. Syst. Technol. 7(2): 17:1-17:3 (2016) - [c15]Bryant Chen, Judea Pearl, Elias Bareinboim:
Incorporating Knowledge into Structural Equation Models Using Auxiliary Variables. IJCAI 2016: 3577-3583 - 2015
- [c14]Elias Bareinboim, Jin Tian:
Recovering Causal Effects from Selection Bias. AAAI 2015: 3475-3481 - [c13]Elias Bareinboim, Andrew Forney, Judea Pearl:
Bandits with Unobserved Confounders: A Causal Approach. NIPS 2015: 1342-1350 - [i4]Judea Pearl, Elias Bareinboim:
External Validity: From Do-Calculus to Transportability Across Populations. CoRR abs/1503.01603 (2015) - [i3]Bryant Chen, Judea Pearl, Elias Bareinboim:
Identification by Auxiliary Instrumental Sets in Linear Structural Equation Models. CoRR abs/1511.02995 (2015) - 2014
- [b1]Elias Bareinboim:
Generalizability in Causal Inference: Theory and Algorithms. University of California, Los Angeles, USA, 2014 - [j2]Elias Bareinboim, Judea Pearl:
Generalizing causal knowledge: theory and algorithms. AI Matters 1(2): 11-13 (2014) - [c12]Elias Bareinboim, Jin Tian, Judea Pearl:
Recovering from Selection Bias in Causal and Statistical Inference. AAAI 2014: 2410-2416 - [c11]Elias Bareinboim, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments: Completeness Results. NIPS 2014: 280-288 - 2013
- [c10]Elias Bareinboim, Judea Pearl:
Causal Transportability with Limited Experiments. AAAI 2013: 95-101 - [c9]Elias Bareinboim, Judea Pearl:
Meta-Transportability of Causal Effects: A Formal Approach. AISTATS 2013: 135-143 - [c8]Elias Bareinboim, Sanghack Lee, Vasant G. Honavar, Judea Pearl:
Transportability from Multiple Environments with Limited Experiments. NIPS 2013: 136-144 - [i2]Elias Bareinboim, Judea Pearl:
A General Algorithm for Deciding Transportability of Experimental Results. CoRR abs/1312.7485 (2013) - 2012
- [c7]Elias Bareinboim, Judea Pearl:
Transportability of Causal Effects: Completeness Results. AAAI 2012: 698-704 - [c6]Elias Bareinboim, Judea Pearl:
Causal Inference by Surrogate Experiments: z-Identifiability. UAI 2012: 113-120 - [c5]Elias Bareinboim, Judea Pearl:
Controlling Selection Bias in Causal Inference. AISTATS 2012: 100-108 - [i1]Elias Bareinboim, Judea Pearl:
Causal Inference by Surrogate Experiments: z-Identifiability. CoRR abs/1210.4842 (2012) - 2011
- [j1]Paulo C. Carvalho, Juliana S. G. Fischer, Jonas Perales, John R. Yates III, Valmir Carneiro Barbosa, Elias Bareinboim:
Analyzing marginal cases in differential shotgun proteomics. Bioinform. 27(2): 275-276 (2011) - [c4]Judea Pearl, Elias Bareinboim:
Transportability of Causal and Statistical Relations: A Formal Approach. AAAI 2011: 247-254 - [c3]Elias Bareinboim, Judea Pearl:
Controlling Selection Bias in Causal Inference. AAAI 2011: 1754-1755 - [c2]Elias Bareinboim, Carlos Brito, Judea Pearl:
Local Characterizations of Causal Bayesian Networks. GKR 2011: 1-17 - [c1]Judea Pearl, Elias Bareinboim:
Transportability of Causal and Statistical Relations: A Formal Approach. ICDM Workshops 2011: 540-547
Coauthor Index
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