Publications

I have published papers in AI and ML conferences (e.g., UAI, NeurIPS, ICML, AAAI). Within the scope of causal inference, I investigated the problem of causal effect identifiability allowing causal inference engines to take more diverse sets of data. In parallel, I also studied on the identifiability problem for heterogeneous domains (similar to the underlying ideas of domain adaptation or transfer learning in ML) called transportability . I also pursue research in decision-making 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 such as relational conditional independence, relational Markov equivalence classes, relational causal discovery algorithms both theoretical and practical.

[Google Scholar]

Collaborators: Juan D. Correa, Aria Khademi, Elias Bareinboim, Vasant Honavar

* for joint first authorship

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

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

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

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

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

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

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

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

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

Pre-Ph.D. —

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

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

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

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

“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

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

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

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

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 Second International Congress on Big Data