SERious EPI is a podcast hosted by Hailey Banack and Matt Fox where leading epidemiology researchers are interviewed on cutting edge and novel methods. Interviews focus on why these methods are so important, what problems they solve, and how they are currently being used.
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In this episode of SERious Epidemiology , Hailey and Matt are joined by Dr. Louisa Smith to discuss Chapter 8 of Causal Inference: What If on selection bias. The conversation explores how selection bias can arise through conditioning on a collider, how it differs from confounding, and how loss to follow-up and censoring can introduce bias even in randomized trials. A major theme of this episode is…
In this episode, we talk with Dr. Alexis Reeves about Chapter 9 of Causal Inference: What If , focusing on measurement bias (the bias formerly known as information bias). Measurement bias arises when exposures, outcomes, confounders, or colliders are measured incorrectly. We discuss different types of measurement bias: differential, nondifferential, dependent, and independent, and errors in…
“Confounding, Confounding, Confounding” is like the epidemiologist’s version of “Marcia, Marcia, Marcia” from the Brady Bunch. To discuss Chapter 7 of Causal Inference: What If , we welcome Dr. Ashley Naimi. In this chapter, we discuss confounding as a central problem when estimating causal effects from observational data. The chapter emphasizes that confounding is not just an imbalance in…
In this episode of SERious Epidemiology, Hailey and Matt welcome guest host Dr. John Jackson to discuss Chapter 5 of Causal Inference: What If? This chapter focuses on explaining the concept of interaction. Together, they unpack the often-confusing distinction between causal interaction and effect measure modification. Throughout the discussion they go on (helpful) tangents to talk about factorial…
Hailey and Matt are joined by guest co-host Dr. Mabel Carabali to discuss Chapter 4 (Effect Modification) from Causal Inference: What If . We start off our discussion about heterogeneity of treatment effects, emphasizing that there is often no single causal effect but effects that vary across groups depending on population characteristics. Mabel helps to explain effect (measure) modification as…
In this episode of SERious Epidemiology, Matt and Hailey welcome guest Dr. Peter Tennant to discuss chapters 2 and 3 of Causal Inference: What If. After learning about Peter’s late ‑discovered love of cashew nuts despite past nut allergies, we shift to a discussion about observational studies and randomized trials. Like the textbook, we start talking about why randomized trials are a helpful…
Welcome back to SERious Epidemiology! This is the first official episode of the fifth season of SERious Epidemiology. This season we’ll be discussing the textbook Causal Inference: What If . Chapter 1 discusses foundational concepts of causal inference, including identifiability assumptions, counterfactuals, individual vs. population-level causal effects, and null effects. We talk about the…
Welcome back to SERious Epidemiology! This episode sets the stage for the fifth season of our podcast. We are excited to announce that Season 5 will be focused on the textbook Causal Inference: What If by Miguel Hernan and James Robins. In this intro episode we chat with Dr. Hernán, discuss what motivated the authors to write this book, and provide a big-picture overview of the textbook so you can…
In an episode recorded before the US presidential elections (somehow) Matt and Hailey end season 4 with a discussion of agent based models, following on from our previous conversation with Dr. Brandon Marshall on the topic. This was perhaps the hardest solo conversations we’ve had as neither of us have much experience with them, but we are both really fascinated by them. We discuss their role in…
In this episode, we discuss Agent Based Models with Dr. Brandon Marshall of the Brown School of Public Health. We talk about what these models are and why they are so useful in epidemiology. We discuss the challenges with these models and how to improve them. We talk about microsimulations and their relationship to mathematical models like SIR models. We talk about how the fit into the world of…
