Jeroen Mulder is a postdoctoral researchers working in the Dynamic Modeling Lab of prof. Dr. Ellen Hamaker, and as part of the Stress in Action research consortium. His project is concerned with the development and evaluation of mathematical models to study causal effects from longitudinal, observational data (both panel data, as well as intensive longitudinal data).

Currently, he is working on (a) the use machine learning techniques to estimate propensity scores in longitudinal models, which can then be used in causal inference in psychological research; and (b) the comparison of causal inference methods from epidemiology and biostatistics (e.g., g-methods such as inverse probability weighting estimation of marginal structural models, and structural nested mean models), to (continuous-time) structural equation modeling methods popular in psychological research. The later topic continues work that he has done as part of his PhD project.

Jeroen has additionally published on extensions of the random intercept cross-lagged panel model (RI-CPLM, creating an extensive supporting website containing Mplus and lavaan code), power analysis for the RI-CLPM (creating the R-package powRICLPM), and has been involved in multiple applied psychological research projects.