I am an Assistant Professor in the Graduate School of Data Science at Seoul National University, directing Causality Lab . Prior to joining Seoul National University, I was an Associate Research Scientist at Columbia University and Postdoctoral Research Associate at Computer Science, Purdue University working with Prof. Elias Bareinboim. I got my Ph.D. in College of Information Sciences and Technology, Pennsylvania State University, University Park, under the supervision of Prof. Vasant Honavar.

Currently, I am working on the theory and application of causality from the perspectives of machine learning, artificial intelligence, and data science. In particular, I am interested in causal discovery, sequential decision-making, and the identifiability and estimation of causal effects in diverse settings. During my Ph.D. study, I focused on causal discovery in a relational domain.
[Curriculum Vitae]

Causality lab. is seeking self-motivated undergraduate interns with strong problem-solving abilities and excellent communication skills who can work in a collaborative environment. Please email me if you are interested in solving problems in causal inference (check out Research Topics below).

Research Interests

• Causal Inference (how can we identify and estimate the effect of an intervention?), Causal Decision Making (how can we utilize causal information in decision making?), Causal Discovery (how can we establish causal relationships from complex data?)
• (Future) Developing theories and applications for health and social domains with causality as a first principle. Developing robust machine learning models and algorithms utilizing causal knowledge. Artificial general intelligence and causality (how can an agent equip a causal mindset. how to counterfactually reason about its actions and consequences.)

Employment & Education

News

• (Oct 2024) Two NeurIPS workshop papers are accepted.
• (Sep 2024) A paper on a graphical criterion for sequential adjustment is accepted at NeurIPS!
• (Aug 2024) Congratulations, Hyeonji, on receiving NRF’s Research Scholarship for Master students!
• (Aug 2024) Congratulations to Inwoo for being chosen as a recipient of the Yulchon AI STAR Scholarship!
• (Jun 2024) Six students joined Causality Lab!
• (May 2024) Two papers are accepted at ICML 2024. Thanks for students’ incredible efforts!
• (Apr 2024) Two papers are accepted at UAI 2024. Super congrats!
• (Jan 2024) Soheun’s paper on cyclic causal discovery is accepted at AISTATS. Congrats!
• (Nov 2023) Two NeurIPS workshop papers are accepted.
• (Jun 2023) Causal dynamics learning paper by Inwoo is accepted to a workshop at ICML 2023.
• (Jan 2023) Inwoo’s paper on local causal discovery using neural network is accepted at CLeaR 2023!

Academic Activities

Program Committee/Reviewed for

• 2025 AAAI, KDD, ICLR, AISTATS, CLeaR
• 2024 NeurIPS (Area Chair), CLeaR, ICML, UAI, JMLR, ECAI, ARR (June), CI4TS at UAI
• 2023 UAI, NeurIPS (top reviewer), Journal of Causal Inference (JCI), CI4TS at UAI
• 2022 ICLR (highlighted reviewer), AAAI, AISTATS, CLeaR, ICML, UAI (top reviewer), JCI, NeurIPS
• 2021 ICLR, AAAI, AISTATS, UAI, ICML, NeurIPS, JAIR, Why now? workshop at NeurIPS
• 2020 NeurIPS, UAI, ICML (top reviewer), AAAI, AISTATS, IEEE TPAMI, Journal of Artificial Intelligence (AIJ), JCI, CDML Workshop at NeurIPS
• 2019 NeurIPS (Best Reviewer Award), WHY conference, JMLR, 2017 Causality Workshop at UAI, 2016 ACM CHI, 2014 ACM TIST

Research Projects

• Causal Machine Learning (NRF, PI, 2023~27 with Innovative Research Lab Initiation Grant)
• Self-Motivated AI: Developing self-directed AI agents that can solve new problems (IITP, Co-I, 2022~26, PI: Byoung-Tak Zhang)
• Center for Optimizing Hyperscale AI Models and Platforms (NRF, Co-I, 2023.06~, PI: Jaejin Lee)
• Metabolomic Big Data Analysis (MFDS, Co-I, 2023~25)
• An algorithmic aspect of proxy-based causal inference (SNU, PI, 2021~24)
• Deep Generative Models for Causal Reasoning (LG AI Research, PI, 2024.05~25.05)

Causality Lab.

Research Topics

• Non-stationary Temporal Causal Discovery
• Representation Learning for Causal Inference
• Explainability/Interpretability for Causal Inference (Uncertainty Quantification)
• Efficient Causal Decision Making in Generalized Settings (Non-stationary, without Sufficiency)
• Deep Generative Models for Temporal Causal Inference
• Causal Inference/Discovery with Text/LLM
• misc. (fairness, recommender systems, domain generalization, RAG, … )

Ph.D. candidates

• Inwoo Hwang, causal machine learning, trustworthy AI
• Yeha Kim, deep counterfactual inference
• Juhyeon Kim, causal NLP

Ph.D. students

• Yesong Choe, efficient causal inference
• Yeahoon Kwon, causal imitation learning
• Min Woo Park, causal decision making
• Yeo Dong Youn, causal NLP, causal ML
• Byeonghui Lim, causal decision making
• Jonghwan Kim, robust and efficient causal discovery
• Jung Soo Kim, representation learning for instrumental variables

Master students

• Taehan Kim, causal feature selection
• Jaeho Jeong, robust conformal inference of individual treatment effects
• Heejin Choi, interpretable causal inference
• Hyunwoo Park
• Oh Yoon Kwon
• Soungmin Park, causal RL, counterfactual Identification
• Jin A Choi causal explanation
• Kwon Ho Kim, causal discovery with score matching, causally disentangled representation learning
• Jihae Chung
• Hyeonji Kim
• Sujeong Oh, causal machine learning
• Daehui Park, causal inference
• Younsuk Yeom, causal discovery, causal representation learning
• Eunseo Lee
• Sumin Cho
• Sangyeon Cho, causal data science for economic analysis
• Min Young Cho

Undergraduate Researchers

• Taehui Yun (Fall, 2024)

Alumni

• Kyung A Song (master 2022)
• Dahhee Yim (master 2022, Doosan Enerbility)
• Jeong Ha Moon (master 2022, Coupang)
• Jeongsup Park (master 2022, AB180)
• Chaeyoung Chung (master 2021, secret)
• Dong Kyu Cho (master 2021, → NYU Ph.D. )
• Jewon Kang (master 2021~2023, KDB)
• Soheun Yi (→ CMU Ph.D.)

Past Projects

• Semantic Search for Korean Medical and Legal Documents (SNU, co-PI, 2022~23, PI: Hyopil Shin)
• Association and Causality in Metabolomic data (MFDS, co-PI, 2022)
• Supply Chain based Financial Keyword Analysis (NH Investment, 2021)
• Causal Discovery for Time Series (LG AI Research, PI, 2023.04~24.04)