Causal Inference with Multilevel Models: The Electric Company Example

Estimating causal effects from clustered or grouped data requires careful attention to the hierarchical structure of observations. When units are nested within groups such as students within classrooms, or patients within hospitals—ignoring this structure can lead to incorrect standard errors, inefficient estimates, and invalid causal inferences. Multilevel models provide a principled framework…