Yonghan Jung

Developing causal data science methods to understand causal effects in complex, imperfect data, with broad applications in trustworthy AI and healthcare science.

• Graduate Teaching Award, Purdue University (2023-2024)
• Top Reviewer (NeurIPS 2024, ICML 2022), Best Reviewer (UAI 2023)

Yonghan Jung will be joining the School of Information Sciences at the University of Illinois Urbana-Champaign as an assistant professor on August 16, 2025. His research is centered on causal data science, with the goal of creating and applying artificial intelligence and machine learning (AI/ML) methodologies to reliably estimate causal effects from complex, real-world data.

His work and research interests encompass multiple facets of causal inference. These include the development of general and universal estimation frameworks applicable to computing causal effects in intricate data-generating models; enhancing trustworthy AI through methodologies for quantifying uncertainties using counterfactual analysis, with applications in fairness and explainable AI; addressing causal challenges within complex data-generating processes, such as those involving multimodal datasets or data with elaborate dependencies; and designing causal agents capable of performing end-to-end causal inference, from data collection and understanding the data-generating process to making causality-driven decisions.

He possesses particular expertise in semiparametric causal effect estimation and debiased machine learning, applying these techniques to critical areas like explainable AI and healthcare science. His scientific contributions include advancements toward a general solution for causal effect estimation, providing practical methods for computing complex real-world effects. He earned his PhD in Computer Science from Purdue University.

By appointment, please contact professor

Selected Publications

Jung, Y., Tian, J., & Bareinboim, E. (2024). Unified Covariate Adjustment for Causal Inference. Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS).

Jung, Y., Park, M. W., & Lee, S. (2024). Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference. Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS).

Jung, Y., & Bellot, A. (2024). Efficient Policy Evaluation Across Multiple Different Experimental Datasets. Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS).

Jung, Y., Díaz, I., Tian, J., & Bareinboim, E. (2023). Estimating Causal Effects Identifiable from a Combination of Observations and Experiments. Proceedings of the 37th Annual Conference on Neural Information Processing Systems (NeurIPS).

Jung, Y., Tian, J., & Bareinboim, E. (2023). Estimating Joint Treatment Effects by Combining Multiple Experiments. Proceedings of the 40th International Conference on Machine Learning (ICML).

Jung, Y., Kasiviswanathan, S., Tian, J., Janzing, D., Bloebaum, P., & Bareinboim, E. (2022). On Measuring Causal Contributions via do-interventions. Proceedings of the 39th International Conference on Machine Learning (ICML).

Jung, Y., Tian, J., & Bareinboim, E. (2021). Double Machine Learning Density Estimation for Local Treatment Effects with Instruments. Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS). 

Jung, Y., Tian, J., & Bareinboim, E. (2021). Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. Proceedings of the 38th International Conference on Machine Learning (ICML).

Jung, Y., Tian, J., & Bareinboim, E. (2021). Estimating Identifiable Causal Effects through Double Machine Learning. Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI).

Jung, Y., Tian, J., & Bareinboim, E. (2020). Learning Causal Effects via Weighted Empirical Risk Minimization. Proceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS).

Selected talks

Seminar: "Causal Data Science: Estimating Identifiable Causal Effects," KAIST. (April 2025)

Seminar: "On Measuring Causal Contributions via do-interventions," AI Seminar, Samsung Electronics. (May 2024)

Seminar: "Estimating Joint Treatment Effects from Marginal Experiments," Quantitative Methods Research Seminars, Purdue Business Department. (November 2023)

Tutorial: "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI," Korea Summer Session on Causal Inference. (July 2022)

Lecture Series: "(1) Tutorial on Structural Causal Model, (2) Estimating Any Identifiable Causal Effects, (3) Application of Causality for Human-Centered AI/ML," University of Seoul, Korea. (July 2022)

Tutorial: "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI," Graduate School of Data Science, Seoul National University, Korea. (July 2022)

Tutorial: "Double/Debiased Machine Learning," Naver Clova AI. (July 2022)

Tutorial: "Shortcut learning in Machine Learning: Challenges, Analysis, Solutions," FAccT-22, Seoul, Korea. (June 2022)

Tutorial: "Tutorial on Double/Debiased Machine Learning," AWS Causality Lab, Amazon. (March 2022)

Lecture: "Double/Debiased Machine Learning for causal effect estimation," Causal Inference II (COMS W4775/Spring 2022), Columbia University, USA. (March 2021) (Note: CV lists Mar. 2021, but the course code COMS W4775/Spring 2022 suggests Spring 2022. Please verify the correct year.)