YSPH Biostatistics Seminar: "Causal Inference in the Presence of Interference"

BIS 526 students are required to attend in person.  Others are allowed to attend in person, but may also attend via zoom.

Speaker- Michael Hudgens, Ph.D.

Title- "Causal Inference in the Presence of Interference"

Abstract

A fundamental assumption usually made in causal inference is that of no interference between individuals (or units), i.e., the potential outcomes of one individual are assumed to be unaffected by the treatment assignment of other individuals. However, in many settings, this assumption obviously does not hold. For example, in infectious diseases, whether one person becomes infected may depend on who else in the population is vaccinated. In this talk we will discuss recent approaches to assessing treatment effects in the presence of interference.