  A key challenge in causal inference from observational studies is the
identification and estimation of causal effects in the presence of unmeasured
confounding. In this paper, we introduce a novel approach for causal inference
that leverages information in multiple outcomes to deal with unmeasured
confounding. The key assumption in our approach is conditional independence
among multiple outcomes. In contrast to existing proposals in the literature,
the roles of multiple outcomes in our key identification assumption are
symmetric, hence the name parallel outcomes. We show nonparametric
identifiability with at least three parallel outcomes and provide parametric
estimation tools under a set of linear structural equation models. Our proposal
is evaluated through a set of synthetic and real data analyses.
