Also see my Google Scholar profile page. My PhD thesis can be found here.

Selected publications

• Van Kesteren, E.-J. (Under Review). Tidy simulation: Designing robust, reproducible, and scalable Monte Carlo simulations.
  Preprint code
• Fang, Q., Garcia-Bernardo, J., & Van Kesteren, E.-J. (Submitted). Using Large Language Models for Text Annotation in Social Science and Humanities: A Hands-On Python/R Tutorial
  Preprint
• Custers, G., Van Kesteren, E.-J., & Ryan, O. (Accepted). The limited impact of extending the school week on educational outcomes in Dutch primary education: a quasi-experimental study using penalized synthetic control analysis. International Journal for Research on Extended Education
  Preprint code
• Schram, R. D., Spithorst, S., & Van Kesteren, E.-J. (2025). Metasyn: Transparent Generation of Synthetic Tabular Data with Privacy Guarantees. The Journal of Open Source Software doi: 10.21105/joss.07099
  Preprint code review
• Van Kesteren, E.-J. (2024). To democratize research with sensitive data, we should make synthetic data more accessible. Patterns. doi: 10.1016/j.patter.2024.101049
  Preprint
• Volker, T. B., de Wolf, P. P., & van Kesteren, E.-J. (2023) Assessing the Utility of Synthetic Data: A Density Ratio Perspective UNECE Expert meeting on Statistical Data Confidentiality.
  Preprint code
• Van Kesteren, E.-J., & Bergkamp, T. L. G. (2023). Bayesian Analysis of Formula One Race Results: Disentangling Driver Skill and Constructor Advantage.
  Journal of Quantitative Analysis in Sports. doi: 10.1515/jqas-2022-0021
  Preprint code
• Van Kesteren, E.-J., & Oberski, D. L. (2022). Flexible Extensions to Structural Equation Models using Computation Graphs. Structural Equation Modeling: A Multidisciplinary Journal. doi: 10.1080/10705511.2021.1971527
  Preprint code
• Van Kesteren, E.-J., & Kievit, R. K. (2021). Exploratory factor analysis with structured residuals for brain network data. Network Neuroscience. doi: 10.1162/netn_a_00162
  Preprint code
• Boeschoten, L., Van Kesteren, E.-J., Bagheri, A., & Oberski, D. L. (2020). Achieving Fair Inference Using Error-Prone Outcomes. International Journal of Interactive Multimedia and Artificial Intelligence. doi: 10.9781/ijimai.2021.02.007
  Preprint code
• Van Kesteren, E.-J., & Oberski, D.L. (2019) Mediation analysis with many potential mediators. Structural Equation Modeling: A Multidisciplinary Journal. doi: 10.1080/10705511.2019.1588124
  Preprint code