  We propose a novel definition of selection bias in analytic epidemiology
using potential outcomes. This definition captures selection bias under both
the structural approach (where conditioning on selection into the study opens a
noncausal path from exposure to disease in a directed acyclic graph) and the
traditional definition (where a given measure of association differs between
the study sample and the population eligible for inclusion). It is
nonparametric, and selection bias under this approach can be analyzed using
single-world intervention graphs both under and away from the null hypothesis.
It allows the simultaneous analysis of confounding and selection bias, it
explicitly links the selection of study participants to the estimation of
causal effects using study data, and it can be adapted to handle selection bias
in descriptive epidemiology. Through examples, we show that this approach
provides a novel perspective on the variety of mechanisms that can generate
selection bias and simplifies the analysis of selection bias in matched studies
and case-cohort studies.
