This line of research examines whether established definitions of test fairness can inform algorithmic fairness (and vice versa), and how fairness considerations can be integrated into causal inference methods. Ongoing projects include:

  • (i) exploring how fairness concepts from psychometrics (emerging in the 1960s) can inform modern algorithmic fairness (emerging in the 2010s).
  • (ii) developing a causal framework for item fairness using single world intervention graphs,
  • (iii) developing data-driven policy learning methods under fairness considerations in multilevel data

These projects are supported in part by an NSF grant.

Publications/Working Papers

  • Suk, Y., & Lyu, W. (2026). Identifying causes of test unfairness: Manipulability and separability. arXiv. [Preprint]
  • Suk, Y., & Lyu, W. (2026). Rethinking item fairness using single world intervention graphs. Journal of Educational and Behavioral Statistics. [Journal Article] [Preprint]
  • Suk, Y., Park, C., Pan, C., & Kim, K. (2024). Fair and robust estimation of heterogeneous treatment effects for optimal policies in multilevel studies. PsyArXiv. [Preprint] [R code]
  • Suk, Y., & Han, K. T. (2024). Evaluating intersectional fairness in algorithmic decision making using intersectional differential algorithmic functioning. Journal of Educational and Behavioral Statistics. [Journal Article] [Preprint]
  • 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. [Journal Article] [Preprint] [R code]

Recent Conferences/Seminars

  • Suk, Y., & Lyu, W. (2026, Apr.). Rethinking item fairness using single world intervention graphs.} Paper to be presented at the National Council on Measurement in Education (NCME), Los Angeles, CA, U.S.}
  • Suk, Y., Park, C., Pan., C., & Kim, K. (2025, Oct). Fair and robust estimation of heterogeneous treatment effects in multilevel studies. The Society for Research on Educational Effectiveness (SREE), Chicago, IL, U.S.
  • Suk, Y., Park, C., Pan., C., & Kim, K. (2025, July). Fair and robust estimation of heterogeneous treatment effects in multilevel studies. The International Conference on Education Research (ICER), Seoul, South Korea.
  • Suk, Y., & Lyu, W. (2025, Apr.). Rethinking item fairness with counterfactuals and single-world intervention graphs. The European Causal Inference Meeting (EuroCIM), Ghent, Belgium.
  • Suk, Y., & Han, K. T. (2024, Apr.). A framework for evaluating intersectional fairness in algorithmic decision making. The National Council on Measurement in Education (NCME), Philadelphia, PA, U.S.
  • Suk, Y., Kim, K., & Park, C. (2024, Apr.). Towards fair and personalized education policy: Reducing racial and state disparities in advanced math courses. The American Educational Research Association (AERA), Philadelphia, PA, U.S.
  • Suk, Y., & Han, K. T. (2023, Sep.). A psychometric framework for evaluating fairness in algorithmic decision making: Differential algorithmic functioning. The Society for Research on Educational Effectiveness (SREE), Arlington, VA, U.S.
  • Suk, Y., & Han, K. T. (2023, July). Differential algorithmic functioning: A framework for evaluating fairness in algorithmic decision making. The International Meeting of Psychometric Society (IMPS), College Park, MD, U.S.