  Unobserved confounding presents a major threat to the validity of causal
inference from observational studies. In this paper, we introduce a novel
framework that leverages the information in multiple parallel outcomes for
identification and estimation of causal effects. Under a conditional
independence structure among multiple parallel outcomes, we achieve
nonparametric identification with at least three parallel outcomes. We further
show that under a set of linear structural equation models, causal inference is
possible with two parallel outcomes. We develop accompanying estimating
procedures and evaluate their finite sample performance through simulation
studies and a data application studying the causal effect of the tau protein
level on various types of behavioral deficits.
