Hyunseung Kang
Biographic Data
| ID | 5015072 |
|---|---|
| NAME | Hyunseung Kang |
| GIVEN NAMES | Hyunseung |
| FAMILY NAME | Kang |
| SIGNATURE | KANG H |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0001-5748-2707 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
El Niño-driven flooding and mental health symptomology among adolescents and young adults in Peru
Intensifying storms and inter-annual El Niño events may increase psychological stress and worsen mental health. This study examines the relationship between flood exposure and long-term mental health symptoms among adolescents and young people in Peru, the world’s most affected country by El Niño. We analyzed community and self-reported survey data from the Young Lives Study to contrast mental health in 2016 among youth who lived in communities t…
Tuning Random Forests for Causal Inference under Cluster-Level Unmeasured Confounding
Recently, there has been growing interest in using machine learning methods for causal inference due to their automatic and flexible ability to model the propensity score and the outcome model. However, almost all the machine learning methods for causal inference have been studied under the assumption of no unmeasured confounding and there is little work on handling omitted/unmeasured variable bias. This paper focuses on a machine learning method…
Random Forests Approach for Causal Inference with Clustered Observational Data
There is a growing interest in using machine learning (ML) methods for causal inference due to their (nearly) automatic and flexible ability to model key quantities such as the propensity score or the outcome model. Unfortunately, most ML methods for causal inference have been studied under single-level settings where all individuals are independent of each other and there is little work in using these methods with clustered or nested data, a com…
No prominent works on this page.
Random Forests Approach for Causal Inference with Clustered Observational Data
There is a growing interest in using machine learning (ML) methods for causal inference due to their (nearly) automatic and flexible ability to model key quantities such as the propensity score or the outcome model. Unfortunately, most ML methods for causal inference have been studied under single-level settings where all individuals are independent of each other and there is little work in using these methods with clustered or nested data, a com…
Tuning Random Forests for Causal Inference under Cluster-Level Unmeasured Confounding
Recently, there has been growing interest in using machine learning methods for causal inference due to their automatic and flexible ability to model the propensity score and the outcome model. However, almost all the machine learning methods for causal inference have been studied under the assumption of no unmeasured confounding and there is little work on handling omitted/unmeasured variable bias. This paper focuses on a machine learning method…
El Niño-driven flooding and mental health symptomology among adolescents and young adults in Peru
Intensifying storms and inter-annual El Niño events may increase psychological stress and worsen mental health. This study examines the relationship between flood exposure and long-term mental health symptoms among adolescents and young people in Peru, the world’s most affected country by El Niño. We analyzed community and self-reported survey data from the Young Lives Study to contrast mental health in 2016 among youth who lived in communities t…
Propensity score matching (3 works) · Advanced Causal Inference Techniques (2 works) · Artificial Intelligence (2 works) · Artificial Intelligence (2 works) · Bayesian Modeling and Causal Inference (2 works) · Causal inference (2 works) · Computer Science (2 works) · Econometrics (2 works) · Inference (2 works) · Machine learning (2 works)