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.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15We 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
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
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
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
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
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
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
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