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Yonghan Jung

Graduate Student

Graduate Teaching Assistant


Joined department: Fall 2018


Yonghan Jung is a Ph.D. student in Computer Science at Purdue University. He is advised by Elias Bareinboim. His research centers around estimating the causal effect leveraging modern machine learning methods. Specifically, he has paved the way toward filling the gap between causal effect identification and estimation by developing general families of estimators for any identifiable causal effects.

Selected Publications

[Estimating Causal Effects Using Weighting-Based Estimators] Yonghan Jung, Jin Tian, Elias Bareinboim. AAAI-20. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, 2020. [pdf]

[Learning Causal Effects via Weighted Empirical Risk Minimization] Yonghan Jung, Jin Tian, Elias Bareinboim. NeurIPS-20.  In Proceedings of the 34th Annual Conference on Neural Information Processing Systems, 2020 [pdf]

[Estimating Identifiable Causal Effects through Double Machine Learning] Y.Jung, J. Tian, E. Bareinboim. AAAI-21. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. [Forthcoming]

[Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning] Y.Jung, J. Tian, E. Bareinboim. In Proceedings of the 38th International Conference on Machine Learning, 2021. [pdf]

[Double Machine Learning Density Estimation for Local Treatment Effects with Instruments] Y.Jung, J. Tian, E. Bareinboim. NeurIPS-21. In Proceedings of the 35th Annual Conference on Neural Information Processing Systems, 2021. (Acceptance rate: 26.0%) [pdf] spotlight presentation (one of 3% of 9122 submissions))

Last Updated: Dec 4, 2020 2:03 PM

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