Causal inference comprises the understanding of how a certain condition would change under a specific modification of the steady state of the world. In epidemiology, causal inference attempts to understand the cause of a certain disease at the population level. Statistical inference relates to the distribution of a disease in a given sample and how closely this distribution approximates the population-level distribution. Causal inference requires us to estimate the distribution of a given disease in the population under an intervention: how changing risk factor “X” would change disease distribution “Y.” Throughout history, several theories have been formulated to define and explain causation. Although a consensus on the definition of causation has not been reached, the field has developed tools for epidemiologists in the quest to infer causality, such as the use of directed acyclic graphs and novel analytical approaches. Accordingly, this chapter aims to (1) present an overview of historical theories of causation; (2) discuss the concepts of statistical and causal inference; and (3) present new approaches to infer causality that can be used in oral health epidemiology.