Research

Causal Machine learning, Quasi-experimental Designs, Multilevel Modeling, Optimal Treatment Regimes, Algorithmic Fairness, Analysis of Process Data

– Robust machine learning for causal inference in multilevel observational studies
– Optimal treatment regimes in education for data-driven, personalized learning
– Evaluating testing accommodations with quasi-experimental devices and process data
– Evaluating algorithmic fairness in educational settings

Selected Publications

Methods:

Suk, Y., & Park, C. (2023). Designing optimal, data-driven policies from multisite randomized trials. Psychometrika. [PDF] [Preprint] [R code]
Suk, Y., & Han, K. T. (2023). A psychometric framework for evaluating fairness in algorithmic decision making: differential algorithmic functioning. Journal of Educational and Behavioral Statistics. [PDF] [Preprint] [R code]
Suk, Y. (2023). A within-group approach to ensemble machine learning methods for causal inference in multilevel studies. Journal of Educational and Behavioral Statistics. [PDF] [Preprint] [R code]
Lyu, W., Kim, J.-S., & Suk, Y. (2023). Estimating heterogenous 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]

Applications:

Piasecki, T. M. et al. (2023). Smoking status, nicotine medication, vaccination, and COVID-19 hospital outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) study. Nicotine & Tobacco Research, 25(6), 1184-1193. [PDF]
Nolan, B. et al. (2023). Relations of current and past cancer with severe outcomes among 104,590 hospitalized COVID-19 patients: The COVID EHR cohort at the University of Wisconsin. Cancer Epidemiology, Biomarkers & Prevention, 32(1), 12-21. [PDF]
Fiore, M. C. et al. (2022). The first 20 months of the COVID-19 pandemic: mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems. PLOS ONE. [PDF]
Suk, Y., & Lee, J. (2021). Evaluating the effects of school club activities on collaborative competency using random forests for causal inference. Survey Research, 22(4), 55-78. [PDF] (written in Korean)
Kent, R. D., Eichhorn, J., Wilson, E. M., Suk, Y., Bolt, D. M., & Vorperian, H. K. (2021). Auditory-perceptual features of speech in children and adults with Down syndrome: a speech profile analysis. Journal of Speech, Language, and Hearing Research, 64(4). 1157-1175. [PDF]
Suk, Y., Lyu, W., & Steiner, P. M. (2019, August 1). Review of the book Using Classification and Regression Trees: A Practical Primer, by X. Ma. Teachers College Record, https://www.tcrecord.org ID Number: 23015. [PDF]
Suk, Y., & Kim, J.-S. (2019). Measuring the heterogeneity of treatment effects with multilevel observational data. In Wiberg, M., Culpepper, S., Janssen, R., Gonzalez, J., & Molenaar, D (Eds.), Quantitative psychology research: The 83rd annual meeting of the psychometric society (pp. 265-277). New York, NY: Springer. [PDF]
Kim, J.-S., & Suk, Y. (2019). Specifying multilevel mixture selection models in propensity score analysis. In Wiberg, M., Culpepper, S., Janssen, R., Gonzalez, J., & Molenaar, D (Eds.), Quantitative psychology research: The 83rd annual meeting of the psychometric society (pp. 279-291). New York, NY: Springer. [PDF]

Working Papers

Suk, Y., & Han, K. T. (2023+). Evaluating intersectional fairness in algorithmic decision making using intersectional differential algorithmic functioning. PsyArXiv. [Preprint]
Suk, Y., & Kim, Y. (2023+). Fuzzy regression discontinuity designs with multiple control groups under one-sided noncompliance: Evaluating extended time accommodations. PsyArXiv. [Preprint] [R code]
Suk, Y. (2023+). Regression discontinuity designs in education: A practitioner’s guide. PsyArXiv. [Preprint]

Research

Causal Machine learning, Quasi-experimental Designs, Multilevel Modeling, Optimal Treatment Regimes, Algorithmic Fairness, Analysis of Process Data

Selected Publications

Methods:

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

Suk, Y., & Han, K. T. (2023). A psychometric framework for evaluating fairness in algorithmic decision making: differential algorithmic functioning. Journal of Educational and Behavioral Statistics. [PDF] [Preprint] [R code]

Suk, Y. (2023). A within-group approach to ensemble machine learning methods for causal inference in multilevel studies. Journal of Educational and Behavioral Statistics. [PDF] [Preprint] [R code]

Lyu, W., Kim, J.-S., & Suk, Y. (2023). Estimating heterogenous 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]

Applications:

Piasecki, T. M. et al. (2023). Smoking status, nicotine medication, vaccination, and COVID-19 hospital outcomes: Findings from the COVID EHR Cohort at the University of Wisconsin (CEC-UW) study. Nicotine & Tobacco Research, 25(6), 1184-1193. [PDF]

Nolan, B. et al. (2023). Relations of current and past cancer with severe outcomes among 104,590 hospitalized COVID-19 patients: The COVID EHR cohort at the University of Wisconsin. Cancer Epidemiology, Biomarkers & Prevention, 32(1), 12-21. [PDF]

Fiore, M. C. et al. (2022). The first 20 months of the COVID-19 pandemic: mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems. PLOS ONE. [PDF]

Suk, Y., & Lee, J. (2021). Evaluating the effects of school club activities on collaborative competency using random forests for causal inference. Survey Research, 22(4), 55-78. [PDF] (written in Korean)

Kent, R. D., Eichhorn, J., Wilson, E. M., Suk, Y., Bolt, D. M., & Vorperian, H. K. (2021). Auditory-perceptual features of speech in children and adults with Down syndrome: a speech profile analysis. Journal of Speech, Language, and Hearing Research, 64(4). 1157-1175. [PDF]

Suk, Y., Lyu, W., & Steiner, P. M. (2019, August 1). Review of the book Using Classification and Regression Trees: A Practical Primer, by X. Ma. Teachers College Record, https://www.tcrecord.org ID Number: 23015. [PDF]

Working Papers

Suk, Y., & Han, K. T. (2023+). Evaluating intersectional fairness in algorithmic decision making using intersectional differential algorithmic functioning. PsyArXiv. [Preprint]

Suk, Y., & Kim, Y. (2023+). Fuzzy regression discontinuity designs with multiple control groups under one-sided noncompliance: Evaluating extended time accommodations. PsyArXiv. [Preprint] [R code]

Suk, Y. (2023+). Regression discontinuity designs in education: A practitioner’s guide. PsyArXiv. [Preprint]