SERious EPI

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.