Suk, Youmi (ys2952)

Ph.D., Educational Psychology (Quantitative Methods), University of Wisconsin-Madison, 2021

M.S., Statistics, University of Wisconsin-Madison, 2019

M.A., Education (Educational Measurement and Evaluation), Seoul National University, 2016

B.S., Earth Science Education, Seoul National University, 2014

Youmi Suk is an Assistant Professor in the Measurement, Evaluation, and Statistics program in the Department of Human Development at Teachers College, Columbia University. She is also a member of the Data Science Institute (DSI) at Columbia University. Prior to joining Teachers College, she was an Assistant Professor in the School of Data Science at the University of Virginia. Dr. Suk’s research areas include causal machine learning, multilevel modeling, optimal treatment regimes, algorithmic fairness, and analysis of process data. Her current research projects fall into four categories: (i) robust machine learning for causal inference in multilevel observational studies, (ii) optimal treatment regimes in education for data-driven, personalized learning, (iii) evaluating testing accommodations with quasi-experimental devices and process data, and (iv) evaluating algorithmic fairness in educational settings. Dr. Suk has received awards and grants for her work, including the National Science Foundation (NSF) and American Educational Research Association (AERA). Also, she has taught courses on linear regression, data visualization, and programming for data science.

Suk, Y. (2024). Regression Discontinuity Designs in Education: A Practitioner's Guide. Asian Pacific Education Research. [PDF] [Preprint] [R Code]

Suk, Y., & Han, K. T. (2024). A psychometric framework for evaluating fairness in algorithmic decision making: Differential algorithmic functioning. Journal of Educational and Behavioral Statistics, 49(2), 151-172. [PDF] [Preprint] [R Code]

Suk, Y. (2024). A within-group approach to ensemble machine learning methods for causal inference in multilevel studies. Journal of Educational and Behavioral Statistics, 49(1), 61-91. [PDF] [Preprint] [R Code]

Suk, Y., & Park, C. (2023). Designing optimal, data-driven policies from multisite randomized trials. Psychometrika. [PDF] [Preprint] [R Code]

Lyu, W., Kim, J.-S., & Suk, Y. (2023). Estimating heterogeneous treatment effects within latent class multilevel models: A Bayesian approach. Journal of Educational and Behavioral Statistics, 48(1), 3-36. [PDF] 

Suk, Y., & Kang, H. (2023). Tuning random forests for causal inference under cluster-level unmeasured confounding. Multivariate Behavioral Research, 58(2), 408-440. [PDF] [Preprint] [R Code] 

Suk, Y., Steiner, P. M., Kim, J.-S., & Kang, H. (2022). Regression discontinuity designs with an ordinal running variable: Evaluating the effects of extended time accommodations for English-language learners. Journal of Educational and Behavioral Statistics. 47(4), 459-484. [PDF] [Preprint] 

Suk, Y., & Kang, H. (2022). Robust machine learning for treatment effects in multilevel observational studies under cluster-level unmeasured confounding. Psychometrika, 87(1), 310–343. [PDF] [Preprint] [R Code] [R Package]

Suk, Y., Kang, H., & Kim, J.-S. (2021). Random forests approach for causal inference with clustered observational data. Multivariate Behavioral Research, 56(6), 829-852. [PDF] [Preprint] [R Code]

Suk, Y., Kim, J.-S., & Kang, H. (2021). Hybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes. Journal of Educational and Behavioral Statistics, 46(3), 323-347. [PDF] [Preprint]