This line of research focuses on developing and applying causal inference methods in educational settings, by integrating methodologies from quasi-experimental designs, machine learning, and multilevel modeling. Ongoing projects include:

  • (1) advancing regression discontinuity designs tailored to educational settings and integrating them with other quasi-experimental designs
  • (2) developing robust machine learning for causal inference in multilevel observational studies

These projects have been supported by an AERA Division D grant and an AERA-NSF grant for early-career scholars.

Publications/Working Papers

  • Lee, Y., & Suk, Y. (2024). Evidence factors in fuzzy regression discontinuity designs with sequential treatment assignments. PsyArXiv. [Preprint]
  • Suk, Y., & Kim, Y. (2024). Fuzzy regression discontinuity designs with multiple control groups under one-sided noncompliance: Evaluating extended time accommodations. Journal of Educational and Behavioral Statistics. [Journal Article] [Preprint] [R Code]
  • Suk, Y. (2024). Regression discontinuity designs in education: A practitioner’s guide. Asia Pacific Education Review, 25, 629-645. [Journal Article] [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. [Journal Article] [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. [Journal Article]
  • Suk, Y., & Kang, H. (2023). Tuning random forests for causal inference under cluster-level unmeasured confounding. Multivariate Behavioral Research, 58(2), 408-440. [Journal Article] [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. [Journal Article] [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. [Journal Article] [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. [Journal Article] [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. [Journal Article] [Preprint]

Recent Conferences/Seminars

  • Suk, Y., & Lee, Y. (2024, Sep.). Evidence factors in fuzzy regression discontinuity designs with multiple control groups for evaluating extended time accommodations. The Society for Research on Educational Effectiveness (SREE), Baltimore, MD, U.S
  • Suk, Y., & Lee, Y. (2024, May). Evidence factors in fuzzy regression discontinuity designs with multiple control groups for evaluating testing accommodations. The American Causal Inference Conference (ACIC), Seattle, WA, U.S.
  • Suk, Y., & Kim, Y. (2024, May). Blessing of multiple control groups in fuzzy regression discontinuity designs: Evaluating extended time accommodations. Paper presented at the American Causal Inference Conference (ACIC), Seattle, WA, U.S. (2024 ACIC Tom Ten Have Award with Honorable Mention)
  • Suk, Y., & Kim, Y. (2023, Sep.). Fuzzy regression discontinuity designs with multiple control groups for evaluating extended time accommodations. The Society for Research on Educational Effectiveness (SREE), Arlington, VA, U.S.
  • Suk, Y. (2023, Apr.). A within-group approach to ensemble machine learning methods for causal inference in multilevel studies. The American Educational Research Association (AERA), Chicago, IL, U.S. (Invited talk as a recipient of the AERA-NSF early-career scholar grant)