The Promises of Parallel Outcomes

A key challenge in causal inference from observational studies is the identification of causal effects in the presence of unmeasured confounding. In this paper, we introduce a novel framework that leverages information in multiple parallel outcomes for causal identification with unmeasured confounding. Under a conditional independence structure among multiple parallel outcomes, we achieve nonparametric identification of causal effects with at least three parallel outcomes. Our identification results pave the road for causal effect estimation with multiple outcomes. In the Supplementary Material, we illustrate the promises of this framework by developing nonparametric estimating procedures in the discrete case, and evaluating their finite sample performance through numerical studies.