Promises of parallel outcomes
本文提出一种利用多个结果变量处理未测量混杂的因果推断新方法，要求结果间条件独立且角色对称，称为平行结果。证明了至少三个平行结果下的非参数可识别性，并提供了线性结构方程模型下的参数估计工具。
Summary 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. An important assumption in our approach is conditional independence among multiple outcomes. In contrast to existing proposals in the literature, the roles of multiple outcomes in the conditional independence 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.