Publications

My collaborators and I have published papers in AI and ML conferences (e.g., UAI, NeurIPS, ICML, AAAI, AISTATS). Within the scope of causal inference, we investigated the problem of causal effect identifiability ID allowing causal inference engines to take more diverse sets of data. In parallel, we also studied on the identifiability problem for heterogeneous domains (similar to the underlying ideas of domain adaptation or transfer learning in ML) called transportability TR. We also pursue research on decision-making DM where causality serves as the first principle to solve the problem. During my PhD study, I mostly spent time on understanding causal discovery from relational data REL such as relational conditional independence, relational Markov equivalence classes, relational causal discovery algorithms both theoretical and practical. Recently, I am interested in the intersection of causality and machine learning CML (e.g., causal representation learning) and CD causal discovery.

[Google Scholar], [Openreview]

Preprints

DM Structural Causal Bandits under Markov Equivalence [paper]
Min Woo Park, Andy Arditi, Elias Bareinboim*, Sanghack Lee*

DM An Introduction to Causal Reinforcement Learning [paper]
Elias Bareinboim, Junzhe Zhang, Sanghack Lee

Published Papers

* for joint first authorship or corresponding author

ws CMLTowards Causal Representation Learning with Observable Sources as Auxiliaries
Kwonho Kim, Heejeong Nam, Inwoo Hwang, Sanghack Lee*
ICML 2025 Workshop on Scaling Up Intervention Models & UAI 2025 Workshop on Causal Abstractions and Representations

CML PEER pressure: Model-to-Model Regularization for Single Source Domain Generalization [paper]
Dong Kyu Cho, Inwoo Hwang, Sanghack Lee*
CVPR 2025

DM Toward a Complete Criterion for Value of Information in Insoluble Decision Problems [paper]
Ryan Carey, Sanghack Lee, Robin J. Evans
Transactions on Machine Learning 2024

ws CD On Incorporating Prior Knowledge Extracted from Pre-trained Language Models into Causal Discovery
Chanhui Lee*, Juhyeon Kim*, YongJun Jeong, Yeom Yoon Seok, Juhyun Lyu, Jung-Hee Kim, Sangmin Lee, Sangjun Han, Hyeokjun Choe, Soyeon Park, Woohyung Lim, Kyunghoon Bae, Sungbin Lim*, Sanghack Lee*
First Workshop on Causality and Large Model at NeurIPS 2024

ws ML Locality-aware Concept Bottleneck Model
Sujin Jeon, Inwoo Hwang, Sanghack Lee*, Byoung-Tak Zhang*
UniReps: 2nd Edition of the Workshop on Unifying Representations in Neural Models at NeurIPS 2024

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

CD DM CML Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning [paper], [poster]
Inwoo Hwang, Yunhyeok Kwak, Suhyung Choi, Byoung-Tak Zhang*, Sanghack Lee*
ICML 2024 (previously, GenPlan workshop at NeurIPS 2023, SCIS workshop at ICML 2023)

ID On Positivity Condition for Causal Inference [paper], [poster]
Inwoo Hwang*, Yesong Choe*, Yeahoon Kwon, Sanghack Lee*
ICML 2024 (+ Causality workshop at UAI 2024)

CD DM Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction [paper]
Yunhyeok Kwak*, Inwoo Hwang*, Dooyoung Kim, Sanghack Lee*, Byoung-Tak Zhang*
UAI 2024, Oral

CD Causal Discovery with Deductive Reasoning: One Less Problem [paper], [poster]
Jonghwan Kim, Inwoo Hwang, Sanghack Lee*
UAI 2024

CD Filter, Rank, and Prune: Learning Linear Cyclic Gaussian Graphical Models [paper]
Soheun Yi, Sanghack Lee*
AISTATS 2024

ws CML Learning to ignore: Single Source Domain Generalization via Oracle Regularization [paper]
Dong Kyu Cho, Sanghack Lee*
Causal Representation Learning Workshop at NeurIPS 2023

CD CML On Discovery of Local Independence over Continuous Variables via Neural Contextual Decomposition [paper]
Inwoo Hwang, Yunhyeok Kwak, Yeon-Ji Song, Byoung-Tak Zhang*, Sanghack Lee*
CLeaR 2023

ws CML Detecting Causality by Data Augmentation via Part-of-Speech tagging
Juhyeon Kim, Yesong Choe and Sanghack Lee
CASE Workshop at EMNLP 2022

TR Counterfactual Transportability: A Formal Approach [paper]
Juan D. Correa, Sanghack Lee and Elias Bareinboim
ICML 2022

ws CD Partition-based Local Independence Discovery
Inwoo Hwang, Byoung-Tak Zhang, and Sanghack Lee
Causal Inference Challenges in Sequential Decision Making: Bridging Theory and Practice Worksop at NeurIPS 2021

ID Nested Counterfactual Identification from Arbitrary Surrogate Experiments [paper]
Juan D. Correa, Sanghack Lee and Elias Bareinboim
NeurIPS 2021

ID Causal Identification with Matrix Equations [paper]
Sanghack Lee and Elias Bareinboim
NeurIPS 2021, Oral

DM Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe [paper], [slides], [poster]
Sanghack Lee and Elias Bareinboim
NeurIPS 2020

ID Causal Effect Identifiability under Partial-Observability [paper]
Sanghack Lee, and Elias Bareinboim
ICML 2020

TR General Transportability — Synthesizing Experiments from Heterogeneous Domains [paper]
Sanghack Lee, Juan D. Correa, and Elias Bareinboim
AAAI 2020

ID Identifiability from a Combination of Observations and Experiments [paper], [slides]
Sanghack Lee, Juan D. Correa, and Elias Bareinboim
AAAI 2020

ID General Identifiability with Arbitrary Surrogate Experiments [paper] [errata]
Sanghack Lee, Juan D. Correa, and Elias Bareinboim
UAI 2019, Best Paper Award

CD Towards Robust Relational Causal Discovery [paper]
Sanghack Lee and Vasant Honavar
UAI 2019

DM Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality [paper]
Aria Khademi, Sanghack Lee, David Foley, and Vasant Honavar
WWW 2019

DM On Structural Causal Bandit with Non-manipulable Variables [paper], [poster], [slides]
Sanghack Lee and Elias Bareinboim
AAAI 2019, Oral

DM Structural Causal Bandits: Where to Intervene? [paper], [code], [poster]
Sanghack Lee and Elias Bareinboim
NeurIPS 2018

Pre-Ph.D. —

CD CIT A Kernel Conditional Independence Test for Relational Data [code], [paper]
Sanghack Lee and Vasant Honavar
UAI 2017

CIT Self-Discrepancy Conditional Independence Test [code], [paper]
Sanghack Lee and Vasant Honavar
UAI 2017

CD A Characterization of Markov Equivalence Classes for Relational Causal Model with Path Semantics [code], [paper], [appendix]
Sanghack Lee and Vasant Honavar
UAI 2016, Oral

CD On Learning Causal Models from Relational Data [code] [paper]
Sanghack Lee and Vasant Honavar
AAAI 2016, Oral

“Teens are from Mars, Adults are from Venus”: Analyzing and Predicting Age Groups with Behavioral Characteristics in Instagram [paper]
Kyungsik Han, Sanghack Lee, Jin Yea Jang, Yong Jung, and Dongwon Lee
WebSci 2016

ws CD Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning [paper]
Sanghack Lee and Vasant Honavar
UAI 2015 Workshop on Advances in Causal Inference

TR Transportability from Multiple Environments with Limited Experiments [paper]
Elias Bareinboim*, Sanghack Lee*, Vasant Honavar, and Judea Pearl
NeurIPS 2013

TR Causal Transportability of Experiments on Controllable Subsets of Variables: z-Transportability [paper]
Sanghack Lee and Vasant Honavar
UAI 2013

TR m-Transportability: Transportability of a Causal Effect from Multiple Environments
Sanghack Lee and Vasant Honavar
AAAI 2013

Learning Classifiers from Distributional Data
Harris Lin*, Sanghack Lee*, Ngot Bui*, and Vasant Honavar
IEEE International Congress on Big Data