Jung, Yonghan

14 publications

ICLR 2026 Debiased Front-Door Learners for Heterogeneous Effects Yonghan Jung

NeurIPS 2025 Path-Specific Effects for Pulse-Oximetry Guided Decisions in Critical Care Kevin Zhang, Yonghan Jung, Divyat Mahajan, Karthikeyan Shanmugam, Shalmali Joshi

CVPR 2025 Sufficient Invariant Learning for Distribution Shift Taero Kim, Subeen Park, Sungjun Lim, Yonghan Jung, Krikamol Muandet, Kyungwoo Song

NeurIPS 2024 Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference Yonghan Jung, Min Woo Park, Sanghack Lee

NeurIPS 2024 Efficient Policy Evaluation Across Multiple Different Experimental Datasets Yonghan Jung, Alexis Bellot

NeurIPS 2024 Unified Covariate Adjustment for Causal Inference Yonghan Jung, Jin Tian, Elias Bareinboim

NeurIPS 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments Yonghan Jung, Ivan Diaz, Jin Tian, Elias Bareinboim

ICML 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2022 On Measuring Causal Contributions via Do-Interventions Yonghan Jung, Shiva Kasiviswanathan, Jin Tian, Dominik Janzing, Patrick Bloebaum, Elias Bareinboim

NeurIPS 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2021 Estimating Identifiable Causal Effects Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

ICML 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class Through Double Machine Learning Yonghan Jung, Jin Tian, Elias Bareinboim

AAAI 2020 Estimating Causal Effects Using Weighting-Based Estimators Yonghan Jung, Jin Tian, Elias Bareinboim

NeurIPS 2020 Learning Causal Effects via Weighted Empirical Risk Minimization Yonghan Jung, Jin Tian, Elias Bareinboim