Title: Case-Based Causality: An Application of Artificial Intelligence to Epidemiology and Public Health
Presenter: Douglas Weed, MD, MPH, PhD
LUNCH WILL BE PROVIDED
Abstract: Causality in public health is a complex and controversial issue, involving epidemiological and toxicological studies and a family of methods discussed and debated for decades: the general scientific method, study design and statistical methods, and research synthesis methods such as the systematic narrative review, meta-analysis, and criteria-based methods of causation. These have been applied to occupational, environmental, and lifestyle exposures as well as diverse outcomes such as cancer, neurological disorders, cardiovascular diseases, and psychiatric conditions. We bring an artificial intelligence (AI) method—Case-Based Reasoning (CBR)—to bear on the issue of causality. The 5 “Rs” of Case-Based Causality (CBC) will be described and applied to current issues in epidemiology and public health. Also, the relationship of CBC to existing methods of causal inference will be noted as well as the links between CBC and the concept of reliability. Fundamentally, Case-Based Causality is a method for examining whether a body of evidence can be considered causal by the extent to which its characteristics are similar to bodies of evidence from known (i.e. established) causal relationships.