| 2026 | AAAI | Mitigating Length Bias in RLHF Through a Causal Lens. | Hyeonji Kim, Sujeong Oh, Sanghack Lee |
| 2025 | CVPR | PEER Pressure: Model-to-Model Regularization for Single Source Domain Generalization. | Dong Kyu Cho, Inwoo Hwang, Sanghack Lee |
| 2025 | ICDM | On Predicting Post-Click Conversion Rate via Counterfactual Inference. | Junhyung Ahn, Sanghack Lee |
| 2024 | AISTATS | Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models. | Soheun Yi, Sanghack Lee |
| 2024 | ICML | On Positivity Condition for Causal Inference. | Inwoo Hwang, Yesong Choe, Yeahoon Kwon, Sanghack Lee |
| 2024 | ICML | Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning. | Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang, Sanghack Lee |
| 2024 | UAI | Causal Discovery with Deductive Reasoning: One Less Problem. | Jonghwan Kim, Inwoo Hwang, Sanghack Lee |
| 2024 | UAI | Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction. | Yunhyeok Kwak, Inwoo Hwang, Dooyoung Kim, Sanghack Lee, Byoung-Tak Zhang |
| 2022 | ICML | Counterfactual Transportability: A Formal Approach. | Juan D. Correa, Sanghack Lee, Elias Bareinboim |
| 2020 | AAAI | Identifiability from a Combination of Observations and Experiments. | Sanghack Lee, Juan D. Correa, Elias Bareinboim |
| 2020 | AAAI | General Transportability - Synthesizing Observations and Experiments from Heterogeneous Domains. | Sanghack Lee, Juan D. Correa, Elias Bareinboim |
| 2020 | ICML | Causal Effect Identifiability under Partial-Observability. | Sanghack Lee, Elias Bareinboim |
| 2019 | AAAI | Structural Causal Bandits with Non-Manipulable Variables. | Sanghack Lee, Elias Bareinboim |
| 2019 | WWW | Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality. | Aria Khademi, Sanghack Lee, David Foley, Vasant G. Honavar |
| 2019 | UAI | General Identifiability with Arbitrary Surrogate Experiments. | Sanghack Lee, Juan D. Correa, Elias Bareinboim |
| 2019 | UAI | Towards Robust Relational Causal Discovery. | Sanghack Lee, Vasant G. Honavar |
| 2017 | UAI | Self-Discrepancy Conditional Independence Test. | Sanghack Lee, Vasant G. Honavar |
| 2017 | UAI | Towards Conditional Independence Test for Relational Data. | Sanghack Lee, Vasant G. Honavar |
| 2016 | AAAI | On Learning Causal Models from Relational Data. | Sanghack Lee, Vasant G. Honavar |
| 2016 | UAI | A Characterization of Markov Equivalence Classes of Relational Causal Models under Path Semantics. | Sanghack Lee, Vasant G. Honavar |
| 2015 | UAI | Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning. | Sanghack Lee, Vasant G. Honavar |
| 2013 | AAAI | m-Transportability: Transportability of a Causal Effect from Multiple Environments. | Sanghack Lee, Vasant G. Honavar |
| 2013 | BigData | Learning Classifiers from Distributional Data. | Harris T. Lin, Sanghack Lee, Ngot Bui, Vasant G. Honavar |
| 2013 | UAI | Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability. | Sanghack Lee, Vasant G. Honavar |
| 2006 | ADMA | A New Polynomial Time Algorithm for Bayesian Network Structure Learning. | Sanghack Lee, Jihoon Yang, Sungyong Park |
| 2004 | DIS | Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem. | Sanghack Lee, Jihoon Yang, Sung-Yong Park |