Causal Inference in Oral Health Epidemiology

Research output: Contribution to book/anthology/report/proceedingBook chapterResearchpeer-review

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.
Original languageEnglish
Title of host publicationOral Epidemiology : Textbooks in Contemporary Dentistry
EditorsMarco Peres, Jose Leopoldo Ferreira Antunes, Richard Watt
Place of publicationCham
Publication year2021
ISBN (print)978-3-030-50122-8
ISBN (Electronic)978-3-030-50123-5
Publication statusPublished - 2021

See relations at Aarhus University Citationformats

ID: 207146080