ProfessorsProfessors of Purdue UniversityYonghan Jung

Yonghan Jung

Purdue University

H-index: 7

North America-United States

About Yonghan Jung

Yonghan Jung, With an exceptional h-index of 7 and a recent h-index of 7 (since 2020), a distinguished researcher at Purdue University, specializes in the field of Causal Inference, Semiparametric Inference, Interpretable Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

A Short Note on Finite Sample Analysis on Double/Debiased Machine Learning

Estimating Joint Treatment Effects by Combining Multiple Experiments

Estimating Causal Effects Identifiable from a Combination of Observations and Experiments

On measuring causal contributions via do-interventions

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning

Estimating Identifiable Causal Effects through Double Machine Learning

Double Machine Learning Density Estimation for Local Treatment Effects with Instruments

Learning Causal Effects via Weighted Empirical Risk Minimization

Yonghan Jung Information

University	Purdue University
Position	___
Citations(all)	237
Citations(since 2020)	233
Cited By	44
hIndex(all)	7
hIndex(since 2020)	7
i10Index(all)	7
i10Index(since 2020)	7
Email	Access Email
University Profile Page	Purdue University
Google Scholar	View Google Scholar Profile

Yonghan Jung Skills & Research Interests

Causal Inference

Semiparametric Inference

Interpretable Machine Learning

Top articles of Yonghan Jung

Title	Journal	Author(s)	Publication Date
A Short Note on Finite Sample Analysis on Double/Debiased Machine Learning		Yonghan Jung	2023/10/5
Estimating Joint Treatment Effects by Combining Multiple Experiments		Yonghan Jung Jin Tian Elias Bareinboim	2023/7/3
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments	Advances in Neural Information Processing Systems	Yonghan Jung Iván Díaz Jin Tian Elias Bareinboim	2024/2/13
On measuring causal contributions via do-interventions		Yonghan Jung Shiva Kasiviswanathan Jin Tian Dominik Janzing Patrick Blöbaum ...	2022/6/28
Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning		Yonghan Jung Jin Tian Elias Bareinboim	2021/2
Estimating Identifiable Causal Effects through Double Machine Learning	Proceedings of the AAAI Conference on Artificial Intelligence	Yonghan Jung Jin Tian Elias Bareinboim	2021/5/18
Double Machine Learning Density Estimation for Local Treatment Effects with Instruments	Advances in Neural Information Processing Systems	Yonghan Jung Jin Tian Elias Bareinboim	2021/12/6
Learning Causal Effects via Weighted Empirical Risk Minimization		Yonghan Jung Jin Tian Elias Bareinboim	2020/6
Estimating Causal Effects Using Weighting-Based Estimators	Proceedings of the AAAI Conference on Artificial Intelligence	Yonghan Jung Jin Tian Elias Bareinboim	2020/4/3