In this episode of SERious Epidemiology, Hailey and Matt are joined by Lucy D’Agostino McGowan to discuss Chapter 10 of Causal Inference: What If? and the problem of random variability. This episode explores the distinction between random error and systematic bias, what confidence intervals and standard errors actually tell us, and why increasing sample size can improve precision without making a biased estimate any closer to the truth. Lucy offers a statistician’s perspective on why random error may actually be the easier problem, and why epidemiologists should be more concerned about where an estimate is centered than about squeezing out a little more efficiency. We also discuss topics like replication, matching versus weighting, baseline adjustment in randomized trials, p-values in Table 1, and the persistent temptation to overinterpret statistical significance.

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 measurement of continuous variables vs. categorical variables. We follow the structure of the chapter, next discussing DAGs and time-related issues in measurement of variables. We end the episode considering whether accurate measurement should be treated as its own causal identification assumption and by highlighting that mismeasured confounders deserve more attention because adjusting for them may leave residual bias or even worsen bias.

“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 covariates across exposure groups, but a causal problem that depends on the underlying structure of how treatment, outcome, and other variables are related. In this episode, Dr. Naimi helps explain concepts related to confounding, exchangeability, and faithfulness. We (try to) talk through confounding-related DAGs and how they are a useful tool to understand confounding bias.  This episode shows why confounding gets so much attention in epidemiology: it is everywhere, often misunderstood, and, like Marcia Brady, it has a way of stealing the spotlight.

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 trials, risk stratification, DAGs, confounding control, and why students are right to find all of this a bit head-spinning. They also debate additive versus multiplicative interaction, sufficient component causes, causal pies, synergism, antagonism, and whether interaction terms can really tell us anything about mechanisms—or whether they mostly tell us where treatment effects may differ. Along the way, there are excellent examples involving surgery, vaccination, infectious disease. Also, in case you ever wondered, every academic has an ever-growing “papers to read” pile.

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 variation in the exposure outcome effect across levels of a third variable. She also explains the concept of qualitative effect modification. We talk about how these concepts connect to transportability and generalizability. The end of the episode focuses on effect modification on the additive vs. multiplicative scales, continuing our (neverending?) debate about when and why we should care about effect modification on the relative scale versus the absolute scale.

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 framing tool to talk about the identifiability assumptions for causal inference. We then introduce concepts such as marginal and conditional effects, exchangeability, positivity, and consistency. We start to dive into the subtle distinctions in some of these concepts: confounding vs. lack of exchangeability due to random error, the underappreciated practical importance of positivity, and how consistency relates to well‑defined interventions.

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 example of Greek gods and goddesses and the notation (e.g., Y for the outcome) used throughout the textbook. In this episode, we hope to highlight the concept that causal effects involve a comparison. Matt also offers a hot take comparing the identifiability assumptions to the Marvel cinematic universe: exchangeability is Captain America, consistency is the Hulk, positivity Ironman, the no interference assumption is Groot. Do you agree?

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 all get excited about what is to come this season.

A copy of the textbook is available online at: https://miguelhernan.org/whatifbook

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 epi curriculum and whether all epi students should learn them. We discuss what they are and how they are useful in epidemiology as simulations and whether they are like SimCity. We also discuss their relationship with counterfactuals and counterfactual theory. We talk about how we see them as relating to DAGs and feedback loops. And we talk about the no interference assumption as it related to both causal inference and agent based models