Yonghan Jung

Summary

I am an aficionado of Causal Data Science!

I am a Ph.D. student in the Department of Computer Science at Purdue University. I am also a member of the CausalAI lab led by my advisor Elias Bareinboim at Columbia University. My research centers around estimating the causal effect using modern machine learning methods. My interest includes semiparametric causal effect estimation, statistical learning theory, causal decision making and their application to interpretable ML or explainable AI, and healthcare science.

I am fortunate to collaborate with many awesome scholars, including Jin Tian, Shiva Kasiviswanathan, Dominik Janzing, Kyungwoo Song, Sanghyuk Chun, and many others.

+ I am an avid runner🏃 and swimmer🏊‍♂️! Find me in Strava!

News

• 👏 July, 2022. I am nominated as a "Top 10% reviewers" in ICML-22!

• 👏 July, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI" at Korea Summer Session on Causal Inference.

• 👏 July, 2022. I gave a series of lectures on (1) Tutorials on the Structural Causal Model, (2) Estimating Any Identifiable Causal Effects, (3) Application of Causality for Human-Centered AI/ML at the University of Seoul, Korea.

• 👏 July, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI" at the Graduate School of Data Science in Seoul National University.

• 👏 July, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Laerning" at Naver Clova AI.

• 👏 July, 2022. I gave a lecture/tutorial titled "Tutorial on the Structural Causal Model" at Postech AI.

• 👏 June, 2022. I hosted a tutorial titled "Shortcut learning in Machine Learning: Challenges, Analysis, Solutions" in FAccT-2022 in Seoul, with Kyungwoo Song and Sanghyuk Chun. I will present on "Understanding Shortcut Learning through the Lens of Causality & Invariance".

• 👏 May, 2022. Our paper, "On Measuring Causal Contributions via do-interventions", is accepted in the ICML-22!

• 👏 Apr, 2022. I gave a lecture/tutorial titled "Estimating Identifiable Causal Effects" in Trustworthy-AI Reading Group. [link]

• 👏 Mar, 2022. I gave a lecture/tutorial titled "Double/Debiased Machine Laerning" to AWS Causality Lab, Mar. 2022

• 👏 Mar, 2022. I gave a 2.5 hrs of lecture/tutorial titled "Double/Debiased Machine Learning for Estimating Causal Effects" in a course Causal Inference II (COMS W4775/Spring 2022) in Columbia University, USA!

• Nov, 2021. My collaborator, Jin Tian (Iowa State University), presented "Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches", which summarized our works R-5 and R-6, in Online Causal Inference Seminar (OCIS)! I helped in the Q&A session. [slide, discussion, video]

• Oct, 2021. Our paper, "do-Shapley: Towards Causal Interpretation of Model Prediction", is on! This is based on the results of my Summer internship at Amazon.

• Sep, 2021. Our paper, "Double Machine Learning Density Estimation for Local Treatment Effects with Instruments", is accepted in NeurIPS-21! This paper is selected as a 🎊spotlight presentation🎊 (one of 3% of 9122 submissions)!

• Aug, 2021. I presented a tutorial "Causal Inference under the rubric of Structural Causal Model" in Korea Summer Session on Causal Inference 2021! [video (in Korean)]

• July, 2021. I presented our work "Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning" in ICML-21!

• June, 2021. I will be an applied scientist intern at Amazon AWS Causality Lab from Jun-Sep 2021, working with Shiva Kasiviswanathan and Dominik Janzing!

• May, 2021. Our paper, "Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning", is accepted in ICML-21!

• Mar, 2021. I will give a 2.5 hrs of lecture/tutorial titled "Causal effect estimation for arbitrary functional" in a course Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA!

• Feb, 2021. I presented our work "Estimating Identifiable Causal Effects through Double Machine Learning", is in AAAI-21!

• Dec, 2020. Our paper, "Estimating Identifiable Causal Effects through Double Machine Learning", is accepted in AAAI-21!

• Dec, 2020. I presented our work "Learning Causal Effects via Weighted Empirical Risk Minimization in NeurIPS-20!

• Sep, 2020. Our paper, "Learning Causal Effects via Weighted Empirical Risk Minimization", is accepted in NeurIPS-20!

• May, 2020. I completed the course requirements and qualifications of Purdue Computer Science.

• Feb, 2020. I presented our work (poster) on estimating causal effect using weighting-based estimator in AAAI-20.

Talks/Seminars

• Tutorial on Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI, Graduate School of Data Science, Seoul National University, Korea, July. 2022, Korea Summer Session on Causal Inference.

• Lecture Series on (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 on Estimating Identifiable Causal Effects and its Application toward Interpretable ML/AI, Graduate School of Data Science, Seoul National University, Korea, July. 2022

• Tutorial on Double/Debiased Machine Laerning, Naver Clova AI, July. 2022

• Tutorial on Structural Causal Model, Postech AI, Korea, July. 2022.

• Tutorial on Shortcut learning in Machine Learning: Challenges, Analysis, Solutions - Understanding Shortcut Learning through the Lens of Causality & Invariance, FAccT-2022

• Tutorial on Estimating Identifiable Causal Effects, Trustworthy-AI Reading Group, Apr. 2022

• Tutorial on Double/Debiased Machine Laerning, AWS Causality Lab, Mar. 2022

• "Double/Debiased Machine Learning for Estimating Causal Effects", Causal Inference II (COMS W4775/Spring 2022) in Columbia University, USA, Mar. 2022

• "Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches", Online Causal Inference Seminar (OCIS), Nov. 2021. Presenter: Jin Tian (Iowa State University), Q&A: Yonghan Jung, Discussant: Ilya Shpitser (Johns Hopkins University) [slide, discussion, video]

• "Causal Inference under the rubric of Structural Causal Model", Korea Summer Session on Causal Inference, Aug. 2021. [video (in Korean)]

• "Causal effect estimation for arbitrary functional", Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA, Mar. 2021. [pdf]

• Estimating Identifiable Causal Effects through Double Machine Learning, AAAI-21 Poster session, Feb. 2021.

• "Learning Causal Effects via Weighted Empirical Risk Minimization", NeurIPS-20 Poster session, Dec. 2020 [poster]

• "Estimating Causal Effects Using Weighting-Based Estimators", AAAI-20 Poster session, Feb. 2020 [poster]

• "Regenerating Evidence from Landmark Trials in ARDS Using Structural Causal Models on Electronic Health Record", American Thoracic Society International Conference, 2018 [link, abstract, poster]

• "Tutorial: Causal Inference", Industrial Statistics Lab, KAIST, South Korea, Jul. 2018

• "Structural Causal Model (SCM) to Identify Causation from Observational Data", Regenstrief Center for Healthcare Engineering, Purdue University, USA, Jun. 2017 [info]

Academic Services

• Reviewer:

  • Journal: Statistics in Medicine, Statistical Science, European Journal of Operation Research, Journal of Causal Inference, Epidemiology, Biostatistics

  • Conference:, ICML, AAAI, NeurIPS, ICLR, IJCAI, etc.

• Software: Contributing to CausalFusion and Causal101

• External Advisor & Visiting Scholar: Serving as an external advisor to the lab led by Kyungwoo Song.

Teaching

• Graduate Teaching Assistant: CS490 Data Science Capstone

• Graduate Teaching Assistant: CS573 Data Mining (Fall 2021)

• Invited Lecturer: Causal Inference II (COMS W4775/Spring 2021) in Columbia University, USA (Spring 2021) [pdf]

• Graduate Teaching Assistant: CS471 Introduction to Artificial Intelligence (Spring 2021)

• Graduate Teaching Assistant: CS573 Data Mining (Fall 2020)

• Graduate Teaching Assistant: IE383 Integrated Production Systems (Spring 2017)

• Graduate Teaching Assistant: IE383 Integrated Production Systems (Fall 2016)