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Jee-Seon Kim

· Professor, Quantitative Methods Area

University of Wisconsin-Madison · Educational Psychology

Active 2009–2019

h-index6
Citations251
Papers13
Funding
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About

Jee-Seon Kim is a Professor in the Department of Educational Psychology at UW–Madison, specializing in Quantitative Methods. Her role involves advancing research and teaching in educational psychology with a focus on quantitative approaches. As a faculty member, she contributes to the academic community through her expertise in quantitative methods, supporting the development of research in educational psychology and related fields.

Research topics

  • Medicine
  • Psychiatry
  • Family medicine
  • Psychology
  • Nursing

Selected publications

  • Random Forests Approach for Causal Inference with Clustered Observational Data

    Multivariate Behavioral Research · 2020 · 22 citations

    • Computer Science
    • Machine Learning
    • Data Mining

    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 common setting in education studies. This paper investigates using one particular ML method based on random forests known as Causal Forests to estimate treatment effects in multilevel observational data. We conduct simulation studies under different types of multilevel data, including two-level, three-level, and cross-classified data. Our simulation study shows that when the ML method is supplemented with estimated propensity scores from multilevel models that account for clustered/hierarchical structure, the modified ML method outperforms preexisting methods in a wide variety of settings. We conclude by estimating the effect of private math lessons in the Trends in International Mathematics and Science Study data, a large-scale educational assessment where students are nested within schools.

Frequent coauthors

  • Dennis McCarty

    52 shared
  • Andrew Quanbeck

    51 shared
  • Sandy Starr

    Progress Educational Trust

    51 shared
  • Mark Zehner

    University of Wisconsin–Madison

    51 shared
  • Carol Sherbeck

    Ohio Department of Mental Health & Addiction Services

    50 shared
  • Nora Jacobson

    50 shared
  • Todd Molfenter

    10 shared
  • Mark A. Albanese

    University of Wisconsin–Madison

    1 shared

Education

  • Ph.D., Educational Psychology

    University of Wisconsin–Madison

    2000
  • M.S., Educational Psychology

    University of Wisconsin–Madison

    1997
  • B.A., Psychology

    University of Wisconsin–Madison

    1994

Awards & honors

  • Vilas Faculty Mid-Career Investigator Award, UW Office of th…
  • Fellow, National Academy of Education/Spencer Foundation, 20…

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