In this interview, I speak with Dr. Judith Lieber (London School of Hygiene & Tropical Medicine) about her recent paper in The Lancet Global Health examining episiotomy and postpartum haemorrhage in women with moderate or severe anaemia.I originally came across this paper while searching for a real-world example to teach directed acyclic graphs (DAGs). It turned out to be a perfect case: clinically important, analytically rigorous, and explicit about how a DAG guided the study design and adjustment strategy.The study draws on data from the WOMAN-2 trial — a large, international trial of tranexamic acid conducted in Pakistan, Nigeria, Tanzania, and Zambia, focused on postpartum bleeding in women with moderate or severe anaemia. Judy joined the trial team toward the end to conduct exploratory analyses using this rich dataset of over 15,000 women.In this conversation, we focus primarily on methods:-How drawing the DAG clarified the causal question-How it determined what to adjust for — and what to avoid adjusting for-The challenge of distinguishing confounders from mediators-Using proxies when key confounders (like shoulder dystocia) are unmeasured-Conducting a quantitative bias analysis to bound potential unmeasured confounding-Balancing complexity and readability when building a DAGWe also discuss Judy’s pathway into epidemiology, her work at LSHTM’s Clinical Trials Unit, and her current project tackling time-varying treatment decisions with another (even more complicated) DAG.This is a practical, applied conversation about how causal diagrams are actually used in real research — not as theoretical exercises, but as tools for clarifying assumptions, structuring models, and understanding limitations.If you teach causal inference, work with observational data, or are trying to move beyond “control for everything” regression thinking, this is a great example of DAGs in action.