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이상학 교수

Sanghack Lee

서울대학교 데이터사이언스대학원 · 경제학

연구실 소개

이 교수의 연구실은 인과적 추론과 의사결정 이론을 중심으로, 복잡한 시스템에서의 원인과 결과 간 관계를 정량적으로 분석하고 적용하는 데 초점을 맞추고 있습니다. 특히, 인과 모델 기반의 밴딧 문제, 다수의 원천 환경에서의 인과적 일반화(transportability), 그리고 관계형 데이터 기반의 인과 모델 학습 등에서의 이론적 기초와 효율적 알고리즘 개발을 주요 연구 방향으로 삼고 있습니다. 또한, 연구의 영향력과 질을 평가하는 지표로 캐시티션을 분석함으로써 학술 영향력의 메커니즘을 탐구하고 있습니다. 이러한 연구들은 실생활 문제 해결을 위한 정밀한 간섭 전략 설계에 기여합니다.

인과 모델링구조적 밴딧운반 가능성관계형 인과연구 영향력

연구 현황

논문 수
152
총 인용 수
982
최근 5년 논문
24
주요 분야
경제학

연구 성과 추이

표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.

5개년 연도별 논문 게재 수
24총합
2022
2023
2024
2025
2026
5개년 연도별 피인용 수
4총합
20222023202420252026

주요 논문

15
1
논문|인용수 92·1995
Endogenous sharing rules in collective-group rent-seeking
Sanghack Lee
SJR Q1Public Choice
Safety ResearchSocial Sciences
2
논문|인용수 71·2001
Strategic Groups and Rent Dissipation
Kyung Hwan Baik, Sanghack Lee
SJR Q1Economic Inquiry

We consider a rent‐seeking contest in which players can form strategic groups before expending their outlays. We examine the profitability of endogenous group formation and the effect of such group formation on rent dissipation. We show the following: When just one strategic group is formed in equilibrium, group formation is beneficial both to the group members and to the nonmembers, and rent dissipation is smaller than with usual individual rent seeking. However, when more than two strategic gr

Safety ResearchSocial Sciences
3
논문|인용수 63·1997
Collective rent seeking with endogenous group sizes
Kyung Hwan Baik, Sanghack Lee
SJR Q1European Journal of Political Economy
Safety ResearchSocial Sciences
4
book chapter|인용수 40·2004
Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem
Sanghack Lee, Jihoon Yang, Sungyong Park
SJR Q2Lecture notes in computer science
Information SystemsComputer Science
5
논문|인용수 38·1990
International equity markets and trade policy
Sanghack Lee
SJR Q1Journal of International Economics
General Economics, Econometrics and FinanceEconomics, Econometrics and Finance
6
논문|인용수 35·1998
Collective contests with externalities
Sanghack Lee, Jeong-Gyu Kang
SJR Q1European Journal of Political Economy
Safety ResearchSocial Sciences
7
논문|인용수 31·2018
Structural causal bandits: where to intervene?
Sanghack Lee, Elias Bareinboim
neural information processing systems

We study the problem of identifying the best action in a sequential decision-making setting when the reward distributions of the arms exhibit a non-trivial dependence structure, which is governed by the underlying causal model of the domain where the agent is deployed. In this setting, playing an arm corresponds to intervening on a set of variables and setting them to specific values. In this paper, we show that whenever the underlying causal model is not taken into account during the decision-m

Management Science and Operations ResearchDecision Sciences
8
논문|인용수 30·2019
General Identifiability with Arbitrary Surrogate Experiments
Sanghack Lee, Juan D. Correa, Elias Bareinboim
Uncertainty in Artificial Intelligence
Statistics and ProbabilityMathematics
9
논문|인용수 25·2019
Structural Causal Bandits with Non-Manipulable Variables
Sanghack Lee, Elias Bareinboim
Proceedings of the AAAI Conference on Artificial IntelligenceOA

Causal knowledge is sought after throughout data-driven fields due to its explanatory power and potential value to inform decision-making. If the targeted system is well-understood in terms of its causal components, one is able to design more precise and surgical interventions so as to bring certain desired outcomes about. The idea of leveraging the causal understanding of a system to improve decision-making has been studied in the literature under the rubric of structural causal bandits (Lee an

Management Science and Operations ResearchDecision Sciences
10
논문|인용수 22·2016
On Learning Causal Models from Relational Data
Sanghack Lee, Vasant Honavar
Proceedings of the AAAI Conference on Artificial IntelligenceOA

Many applications call for learning causal models from relational data. We investigate Relational Causal Models (RCM) under relational counterparts of adjacency-faithfulness and orientation-faithfulness, yielding a simple approach to identifying a subset of relational d-separation queries needed for determining the structure of an RCM using d-separation against an unrolled DAG representation of the RCM. We provide original theoretical analysis that offers the basis of a sound and efficient algor

Artificial IntelligenceComputer Science
11
논문|인용수 22·2010
Author and article characteristics, journal quality and citation in economic research
Shi Young Lee, Shi Young Lee, Sanghack Lee, Sanghack Lee, Sung Hee Jun
SJR Q3Applied Economics Letters

Citation count serves as an indicator of quality of research in economics as well as in many other disciplines. The purpose of this article is to examine the effects of author and article characteristics on the citations in economics research. We investigate empirically the relationship between citations and author and article characteristics for different journal quality. We found that the same institution (affiliation) and the location of authors may affect the citation positively. Moreover, j

Statistics, Probability and UncertaintyDecision Sciences
12
논문|인용수 21·2013
m-Transportability: Transportability of a Causal Effect from Multiple Environments
Sanghack Lee, Vasant Honavar
Proceedings of the AAAI Conference on Artificial IntelligenceOA

We study m-transportability, a generalization of transportability, which offers a license to use causal information elicited from experiments and observations in m>=1 source environments to estimate a causal effect in a given targetenvironment. We provide a novel characterization of m-transportability that directly exploits the completeness of do-calculus to obtain the necessary and sufficient conditions for m-transportability. We provide an algorithm for deciding m-transportability that dete

Artificial IntelligenceComputer Science
13
논문|인용수 12·2020
General Transportability – Synthesizing Observations and Experiments from Heterogeneous Domains
Sanghack Lee, Juan D. Correa, Elias Bareinboim
Proceedings of the AAAI Conference on Artificial IntelligenceOA

The process of transporting and synthesizing experimental findings from heterogeneous data collections to construct causal explanations is arguably one of the most central and challenging problems in modern data science. This problem has been studied in the causal inference literature under the rubric of causal effect identifiability and transportability (Bareinboim and Pearl 2016). In this paper, we investigate a general version of this challenge where the goal is to learn conditional causal ef

Artificial IntelligenceComputer Science
14
논문|인용수 12·1993
Inter-group competition for a pure private rent
Sanghack Lee
SJR Q2The Quarterly Review of Economics and Finance
Economics and EconometricsEconomics, Econometrics and Finance
15
논문|인용수 11·2006
Contests with size effects through costs
Sanghack Lee
SJR Q1European Journal of Political Economy
AccountingBusiness, Management and Accounting

대표 연구 분야

Artificial IntelligenceEconomics and EconometricsGeneral Economics, Econometrics and FinanceSafety ResearchAerospace EngineeringPolitical Science and International Relations

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