Historical Foundations of Causal Inference in Epidemiology

Empiricist philosophers such as Francis Bacon, John Locke, and David Hume believed that knowledge is gained through observations of natural phenomena. In contrast to deductive logic, inductive logic is not self-contained and therefore is open to error. On the other hand, deductive logic cannot by itself establish a theory of prediction since it has no connection to the natural world. Hume observed that inductive inference does not carry a logical necessity. He challenged the notion that causality could be proved, while highlighting the subjectivity of knowledge and the fallibility of inductive reasoning. In the nineteenth century, Robert Koch provided a framework for identifying acute diseases associated with microorganisms. In the twentieth century, following World War II, efforts were made by Sir Austin Bradford Hill and others to systematize and justify causal inference in observational research. More recent authors, including Mervyn Susser, have offered refined accounts of causal criteria. The Bradford Hill criteria for causal inference or subsets of the criteria are still widely used as a heuristic aid for assessing whether associations observed in epidemiologic research are causal. The model of sufficient component causes proposed by Kenneth Rothman is widely used in epidemiology as a framework for teaching and understanding multicausality.