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.