Epidemiology - As Causal Inference

As Causal Inference

Although epidemiology is sometimes viewed as a collection of statistical tools used to elucidate the associations of exposures to health outcomes, a deeper understanding of this science is that of discovering causal relationships.

It is nearly impossible to say with perfect accuracy how even the most simple physical systems behave beyond the immediate future, much less the complex field of epidemiology, which draws on biology, sociology, mathematics, statistics, anthropology, psychology, and policy; "Correlation does not imply causation" is a common theme for much of the epidemiological literature. For epidemiologists, the key is in the term inference. Epidemiologists use gathered data and a broad range of biomedical and psychosocial theories in an iterative way to generate or expand theory, to test hypotheses, and to make educated, informed assertions about which relationships are causal, and about exactly how they are causal.

Epidemiologists Rothman and Greenland emphasize that the "one cause - one effect" understanding is a simplistic mis-belief. Most outcomes, whether disease or death, are caused by a chain or web consisting of many component causes. Causes can be distinguished as necessary, sufficient or probabilistic conditions. If a necessary condition can be identified and controlled (e.g., antibodies to a disease agent), the harmful outcome can be avoided.