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.

翻译：观察研究得出的因果推论中的一个关键挑战是,在存在未测的混乱的情况下,查明因果关系。在本文件中,我们引入了一个新的框架,在多个平行结果中利用信息进行多重平行结果的因果鉴定,在多个平行结果中,在有条件的独立结构下,我们在多个平行结果中,对因果关系进行非对称鉴定,至少有三个平行结果。我们的鉴定结果为有多重结果的因果估计铺平了道路。在补充材料中,我们通过在离散案件中制定非对称估计程序,并通过数字研究评估其有限的抽样业绩,来说明这一框架的许诺。