Testing an Elaborate Theory of a Causal Hypothesis

Presented by Dylan Small, University of Pennsylvania

Friday, November 11
11:00 a.m. ET
Virtual meeting - Webex meeting room

When R.A. Fisher was asked what can be done in observational studies to clarify the step from association to causation, he replied, “Make your theories elaborate” -- when constructing a causal hypothesis, envisage as many different consequences of its truth as possible and plan observational studies to discover whether each of these consequences is found to hold. William Cochran called “this multi-phasic attack…one of the most potent weapons in observational studies.” Statistical tests for the various pieces of the elaborate theory help to clarify how much the causal hypothesis is corroborated. In practice, the degree of corroboration of the causal hypothesis has been assessed by verbally describing which of the several tests provides evidence for which of the several predictions. This verbal approach can miss quantitative patterns. So, we developed a quantitative approach to making statistical inference about the amount of the elaborate theory that is supported by evidence. This is joint work with Bikram Karmakar.

Speaker Bio:

Dr. Dylan Small is a statistician specializing in observational studies, causal inference and applications to the health and social sciences. Dr. Small is the Universal Furniture Professor and Department Chair in the Department of Statistics and Data Science at the Wharton School of the University of Pennsylvania. He joined the University of Pennsylvania in 2002 after obtaining his Ph.D. in statistics from Stanford University in 1997. Dr. Small is a fellow of the American Statistical Association and was the founding editor of the journal, Observational Studies--the first journal to focus on observational studies.