معرفی
Dr. Felix Clouth is an Assistant Professor in the Department of Methodology and Statistics at Tilburg University's Tilburg School of Social and Behavioral Sciences. He completed his doctoral thesis titled 'Latent class models for causal inference' in March 2024, supervised by Professors Jeroen Vermunt and Steffen Pauws. His academic profile shows active research output with publications spanning from 2019 to 2025, demonstrating consistent scholarly activity in statistical methodology.
Dr. Clouth's research interests focus on the intersection of causal inference and latent variable modeling. His work centers on developing advanced statistical methods that enable valid causal conclusions from observational data when outcomes or exposures are unobservable constructs. Key areas include:
- Latent class analysis for causal inference
- Latent Markov models with the parametric G-Formula
- Inverse propensity weighting techniques
- Bias-adjusted three-step methods
- Applications to patient-reported outcome measures
- Cancer treatment decision support systems
His recent publications show a clear trajectory of methodological innovation with practical applications, particularly in healthcare domains. The 2025 publication on causal inference for latent Markov models represents a significant extension of his work to longitudinal settings, while his 2024 cancer treatment paper demonstrates successful translation of methodological advances into healthcare applications.
Dr. Clouth's scholarly impact is reflected in his growing publication record across high-quality journals in methodology and social sciences. His work contributes to UN Sustainable Development Goals through applications in healthcare decision-making and social science research.
In terms of teaching, Dr. Clouth offers courses in programming, causal analysis techniques, and statistical methods, reflecting his expertise in both methodological development and practical implementation. His 'You R welcome: programming in R' course suggests a focus on making advanced statistical methods accessible to students and researchers.
His collaborative network includes researchers across Tilburg University departments and external partners, as evidenced by his interdisciplinary work on cancer treatment decision support systems. The RepliSims project on simulation study replicability indicates engagement with broader methodological challenges in statistical research.
