| Structural causal bandits: Where to intervene? S Lee, E Bareinboim Advances in neural information processing systems 31, 2018 | 70 | 2018 |
| Fairness in algorithmic decision making: An excursion through the lens of causality A Khademi, S Lee, D Foley, V Honavar The World Wide Web Conference, 2907-2914, 2019 | 67 | 2019 |
| Discovery of hidden similarity on collaborative filtering to overcome sparsity problem S Lee, J Yang, SY Park Discovery Science: 7th International Conference, DS 2004, Padova, Italy …, 2004 | 67 | 2004 |
| General Identifiability with Arbitrary Surrogate Experiments S Lee, JD Correa, E Bareinboim Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019 | 55 | 2019 |
| Structural Causal Bandits with Non-manipulable Variables S Lee, E Bareinboim Thirty-third Conference on Artificial Intelligence (AAAI 2019), 2019 | 42 | 2019 |
| On learning causal models from relational data S Lee, V Honavar Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), 2016 | 30 | 2016 |
| Transportability from multiple environments with limited experiments E Bareinboim, S Lee, V Honavar, J Pearl Advances in Neural Information Processing Systems 26, 2013 | 28 | 2013 |
| m-transportability: Transportability of a causal effect from multiple environments S Lee, V Honavar Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2013), 2013 | 24 | 2013 |
| Generalized Transportability: Synthesis of Experiments from Heterogeneous Domains S Lee, JD Correa, E Bareinboim Thirty-fourth Conference on Artificial Intelligence (AAAI 2020), 2020 | 22 | 2020 |
| Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe S Lee, E Bareinboim Advances in Neural Information Processing Systems 33, 2020 | 19 | 2020 |
| Teens are from mars, adults are from venus: analyzing and predicting age groups with behavioral characteristics in instagram K Han, S Lee, JY Jang, Y Jung, D Lee Proceedings of the 8th ACM Conference on Web Science, 35-44, 2016 | 19 | 2016 |
| Causal Effect Identifiability under Partial-Observability S Lee, E Bareinboim Thirty-seventh International Conference on Machine Learning, 2020 | 17 | 2020 |
| Causal transportability of experiments on controllable subsets of variables: z-transportability S Lee, V Honavar Twenty-ninth Conference on Uncertainty in Artificial Intelligence (UAI 2013 …, 2013 | 14 | 2013 |
| Nested Counterfactual Identification from Arbitrary Surrogate Experiments JD Correa, S Lee, E Bareinboim Advances in Neural Information Processing Systems 34, 2021 | 13 | 2021 |
| Causal Identification with Matrix Equations S Lee, E Bareinboim Advances in Neural Information Processing Systems 34, 2021 | 12 | 2021 |
| A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. S Lee, VG Honavar Thirty-second Conference on Uncertainty in Artificial Intelligence (UAI 2016), 2016 | 12 | 2016 |
| Towards robust relational causal discovery S Lee, V Honavar Thirty-fifth Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019 | 9 | 2019 |
| Self-discrepancy conditional independence test S Lee, VG Honavar Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017 | 8 | 2017 |
| A kernel conditional independence test for relational data S Lee, V Honavar Thirty-third Conference on Uncertainty in Artificial Intelligence (UAI 2017), 2017 | 7 | 2017 |
| Lifted representation of relational causal models revisited: Implications for reasoning and structure learning S Lee, V Honavar UAI 2015 Workshop on Advances in Causal Inference co-located with the 31st …, 2015 | 6 | 2015 |
Elias BareinboimColumbia UniversityVerified email at columbia.edu
