  Defining and identifying causal intervention effects for transmissible
infectious disease outcomes is challenging because a treatment -- such as a
vaccine -- given to one individual may affect the infection outcomes of others.
Epidemiologists have proposed causal estimands to quantify effects of
interventions under contagion using a two-person partnership model. These
simple conceptual models have helped researchers develop causal estimands
relevant to clinical evaluation of vaccine effects. However, many of these
partnership models are formulated under structural assumptions that preclude
realistic infectious disease transmission dynamics, limiting their conceptual
usefulness in defining and identifying causal treatment effects in empirical
intervention trials. In this paper, we propose causal intervention effects in
two-person partnerships under arbitrary infectious disease transmission
dynamics, and give nonparametric identification results showing how effects can
be estimated in empirical trials using time-to-infection or binary outcome
data. The key insight is that contagion is a causal phenomenon that induces
conditional independencies on infection outcomes that can be exploited for the
identification of clinically meaningful causal estimands. These new estimands
are compared to existing quantities, and results are illustrated using a
realistic simulation of an HIV vaccine trial.
