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“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.