For Episode 5, we invited Louise Poppe to introduce us to causal models, with a particular focus on Directed Acyclic Graphs (DAGs). Together, we discuss why researchers in epidemiology — and, in fact, across many scientific disciplines — can benefit from integrating DAGs into their study workflow. We talk about how to get started with causal thinking, common pitfalls and challenges when building DAGs, and what insights these models can provide for study design, analysis, and interpretation.

This episode offers an accessible introduction to causal thinking with DAGs and is a great opportunity to expand your methodological research toolbox.

If this episode sparked your interest and you would like to explore the topic further, here are some helpful resources

• Poppe, L., Steen, J., Loh, W. W., Crombez, G., De Block, F., Jacobs, N., ... & Paepe, A. L. D. (2025). How to develop causal directed acyclic graphs for observational health research: a scoping review. https://doi.org/10.1080/17437199.2024.2402809
• Poppe, L., De Paepe, A. L., Deforche, B., Van Dyck, D., Loeys, T., & Van Cauwenberg, J. (2025). Experience sampling method studies in physical activity research: the relevance of causal reasoning. https://doi.org/10.1186/s12966-025-01723-w 
• Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. https://doi.org/10.1177/251524591774562 
• Online Harvard EdX course by Hernán: https://harvardonline.harvard.edu/course/causal-diagrams-draw-your-assumptions-your-conclusions
• Hernán MA, Robins JM (2020). Causal Inference: What If. Causal Inference: What If (the book) — Miguel Hernán 
• Tennant, P. W., Murray, E. J., Arnold, K. F., Berrie, L., Fox, M. P., Gadd, S. C., ... & Ellison, G. T. (2021). Use of directed acyclic graphs (DAGs) to identify confounders in applied health research: review and recommendations. https://doi.org/10.1093/ije/dyaa213