SERious EPI

Sue Bevan - Society for Epidemiologic Research

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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Avg 51 min
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August 12 · 49 min
S5E8: Selection Bias: Colliders, Censoring, and Confusion
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 the relationship between selection bias and confounding. We also discuss generalizability, target populations, competing events such as death. Finally, we talk about how challenging it is to address selection bias via analytic techniques including inverse probability weighting. This episode serves as a good reminder that when it comes to selection bias, an ounce of prevention is worth a pound of cure.
July 15 · 48 min
S5E7: Exchangeability, Positivity, Consistency… and Measurement?
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.
June 15 · 49 min
S5E6: Confounding is the Marcia Brady of Epidemiology
“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.
May 15 · 44 min
S5E5: Causal Pies, Pizza Toppings, and Interaction
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.
April 15 · 55 min
S5E4: Mind the Modifier: When Causal Effects Refuse to Be Average
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.
March 15 · 55 min
S5E3: From Cashew Nuts to Counterfactuals
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.
February 15 · 51 min
S5E2: Setting the table for Season 5
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?
January 15 · 31 min
S5E1: A Casual Conversation with Miguel Hernán
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
Aug 15, 2025 · 50 min
S4E13: Agent Based Models
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
Jul 15, 2025 · 1 hr
S4E12: Agent Based Models with Dr. Brandon Marshall
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 predictive models but also how they relate to counterfactuals. We talk about how to account for bias in the inputs in these models and how they relate to DAGs and data generating mechanisms. We talk about all the skills needed to create ABMs (coders, modelers, epidemiologists, etc.) and the software used to create them as well as the challenges with and need for replication, calibration and validation. And we talk about how far outside of Matt and Hailey’s experience in epidemiology.
Jun 15, 2025 · 47 min
S4E11: Quantitative Bias Analysis
In this episode we follow up on our conversation with Tim Lash on Quantitative Bias Analysis (QBA), something both Hailey and I have experience with. We talk about what QBA is, why you would want to use it and for what sources of bias it is most applicable. We talk about our own experience with QBA and when we find it most useful. We talk about cases where lots of measurement error leads to little bias and cases where small amounts of measurement error leads to lots of bias. We talk about the overused phrase “non-differential bias towards the null” and why we both hate it. We discuss the impact of bias in terms of direction, magnitude and uncertainty in study results. We talk about the critiques of the methods and when QBA should be done. And we discuss what the role of peer review is (and if it should include QBA). And we discuss Matt’s whether our small talk is useful, our ability to time travel and whether naps are good or bad and if podcasts can nap.
May 15, 2025 · 58 min
S4E10: Quantitative Bias Analysis with Dr. Tim Lash
In this episode we talk to Dr. Timothy Lash of Emory University about Quantitative Bias Analysis (QBA). We talk about how QBA is any method that quantifies the impact of non-random error. We talk about direction magnitude and uncertainty. We differentiate from sensitivity analysis, and we talk about how to identify key sources of bias. We talk about bias models and bias parameters and how we draw inferences from bias analyses. We talk about validation data and where you can get it. We talk about why predictive values often aren’t as useful as classification values for bias analysis. We talk about how bias analysis can strengthen your results and that our intuition about the impact of biases is t always great. And we talk about how bias analysis can guide your future research. We differentiate between simple and probabilistic bias analysis. And we end with some examples of cases where bias analysis is really helpful.
Apr 15, 2025 · 52 min
S4E9: Regression Discontinuity and Difference in Difference(s?)
In this episode Hailey and Matt talk about Matt’s technology troubles (including having his computer just decide not to let him log on) before we discuss regression discontinuity and difference in difference approaches as part of quasi experimental methods. We focus on what quasi experimental means and encompasses and its relation to natural experiments. We talk about who owns interrupted time series (epidemiologists, economists, other social scientists?). Matt again admits he can’t define exogeneity. We talk about how both designs exploit a threshold when there is a rapid change in the probability of being exposed and we think of those on either side of the discontinuity close to the threshold are exchangeable and we can estimate effects in that population under a set of assumptions. And we talk about how difference in difference takes this same approach but adds a control group. And we debate whether the last difference is singular or plural.
Mar 15, 2025 · 55 min
S4E8: Regression Discontinuity and Difference-in-Differences with Dr. Usama Bilal
In this episode we talk to Dr. Usama Bilal of Drexel University about Regression Discontinuity Design (RDD) and Difference-in-Differences (DiD), two quasi experimental methods that fall under the instrumental variables framework which we discussed in previous episodes. We talk about what RDD is, the different types (fuzzy vs sharp) and what we are actually estimating (LATE vs CACE). We talk about the bias vs variance tradeoff in how far from the threshold we choose to draw inferences. We talk about the assumptions that are needed for these methods to give valid estimate of effects. Then we talk about DiD and how this is a form of RDD with a second group that does not experience the discontinuity as a control. And we talk about the additional assumptions needed for this approach (e.g. parallel trends).
Feb 15, 2025 · 49 min
S4E7: Instrumental Variables
In this episode, Hailey and Matt discuss whether IVs are rebellious or magical or the midlife crisis of methods. We talk about how they deal with confounding problems. We talk about how they are used to attempt to mimic randomization and the assumptions for IVs. We talk about why it’s so helpful to think about who gets the exposure and why for causal inference. We talk about how IVs fit in with the target trial framework and wham it might tell us about how to teach intro epi. We talk about what estimand IVs estimate. And we relitigate the soda vs pop discussion.
Jan 15, 2025 · 55 min
S4E6: Instrumental Variables with Dr. Rita Hamad
In this episode, we discuss instrumental variables with Dr. Rita Hamad of Harvard’s TH Chan School of Public Health. This episode is focused on the first part of Chapter 28 of Modern Epidemiology 4th edition on quasi experimental methods. We start with what quasi experimental designs are and why we would want to use them (and whether more epidemiologists are being exposed to them). We also talk about why these methods are more common in economics than in epi. We talk about how these methods try to take advantage of something that approximates randomization to estimate causal effects. We talk about what instrumental variables are and the conditions required to be met for a variable to be an instrument. We focus on the strengths and limitations of the methods and when they make the most sense to use them. We talk about what happens when you violate the assumptions of IV. We talk about weak and strong IVs and we talk about Mendelian randomization and its role in epi. And we ask the age-old question, how do you find the elusive instrumental variable?
Dec 15, 2024 · 52 min
S4E5: Mediation Continuation
In this episode we follow up on our conversation about mediation. We talk about what mediation is and when it is useful. We talk about the history of these methods. We debate what direct and indirect effects are. We describe natural and controlled effects. We discuss the importance of the number 666 in Matt’s life. We talk about exposure mediator interaction. Matt learns what kinesiology is. We discuss proportion mediated and proportion eliminated. And we talk about the confounding assumptions needed for mediation analysis.
Nov 15, 2024 · 58 min
S4E4: Mediation with Kara Rudolph and Ivan Diaz
In this episode, Matt and Hailey talk with Dr. Kara Rudolph and Dr. Ivan Diaz about mediation analysis. We talk through what it is, what it means and when we want to do it. We talk about mechanism of causation and how mediation can help. We cover things like natural direct and indirect effects and controlled direct effects (and why there isn’t a controlled indirect effect – a thing that stumped Matt for some time). And we discuss the different assumptions need to draw valid inferences in a mediation analysis, like all the many no confounding assumptions and the cross world assumption. And we talk about what Matt refers to as mediated moderation (interaction in the effect on the outcome between the exposure and mediator).
Oct 15, 2024 · 53 min
S4E3: How do we define efficiency?
In this episode, Hailey and Matt continue on their discussion on study efficiency and realize that we think about efficiency in very different ways. We talk about the difference between statistical efficiency and cost efficiency and we each make our case for one of them being the driving force in how we design and analyze studies. It may be the biggest disagreement we’ve had yet (though maybe that was interaction).We talk about matching and its impact of efficiency and also why we do matching. And we try to understand when matching is useful. Studies mentioned in the podcast: Rothman KJ, Poole C. A strengthening programme for weak associations. Int J Epidemiol. 1988 Dec;17(4):955-9. doi: 10.1093/ije/17.4.955. PMID: 3225112.
Sep 15, 2024 · 51 min
S4E2: Study Efficiency with Robert Platt
In this episode we are joined by Professor Robert Platt of McGill University to talk about study efficiency and the ways we can think about this in terms of study design. We talk about hierarchies of evidence and its relationship to things like target validity. We get into why we think case control studies are so often misunderstood, particularly with respect to missing that they should be nested within a cohort. We talked about the varying definitions of efficient (variance, efficiency of confounding control, cost efficient, etc.) and how they relate to different study designs, and we disagreed about which definition is the most useful. And we talk about sampling and how it affects study efficiency and also what question we are asking. The paper that Rob reads over and over is: Kurth T, Walker AM, Glynn RJ, Chan KA, Gaziano JM, Berger K, Robins JM. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006;163:262-70. We also referenced: Westreich D, Edwards JK, Lesko CR, Cole SR, Stuart EA. Target Validity and the Hierarchy of Study Designs. Am J Epidemiol. 2019;188:438-443. Kramer MS, Guo T, Platt RW, Shapiro S, Collet JP, Chalmers B, Hodnett E, Sevkovskaya Z, Dzikovich I, Vanilovich I; PROBIT Study Group. Breastfeeding and infant growth: biology or bias? Pediatrics. 2002;110(2 Pt 1):343-7.

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