Dr. Marie Salditt

© Salditt
Position Research Associate
E-Mail msalditt[at]uni-muenster.de          
Telephone +49 (2 51) 83 - XX XX X
Consultation Hours By arrangement via E-Mail     
CV English


  • Research Interests

    • Statistical models for causal inference
    • Machine learning methods for  hierarchical data


  • Publications

    Journal articles (peer-reviewed)

    Eckes, T., Salditt, M., & Nestler, S. (in press). Living up to expectations? A simulation study evaluating methods used to detect sudden gains and sudden losses. Psychological Assessment.

    Nestler, S., & Salditt, M. (in press). Comparing type 1 and type 2 error rates of different tests for heterogeneous treatment effects. Behavior Research Methods.

    Salditt, M., Eckes, T., & Nestler, S. (2023). A tutorial introduction to heterogeneous treatment effect estimation with meta-learners. Administration and Policy in Mental Health and Mental Health Services Research. Advance online publication. https://doi.org/10.1007/s10488-023-01303-9

    Salditt, M., Humberg, S., & Nestler, S. (2023). Gradient tree boosting for hierarchical data. Multivariate Behavioral Research, 58, 911-937. https://doi.org/10.1080/00273171.2022.2146638

    Salditt, M., & Nestler, S. (2023). Parametric and nonparametric propensity score estimation in multilevel observational studies. Statistics in Medicine, 42(23), 4147-4176. https://doi.org/10.1002/sim.985