论文标题
并行结果的承诺
The Promises of Parallel Outcomes
论文作者
论文摘要
观察性研究因果推断的主要挑战是在存在未衡量混杂的情况下鉴定和估计因果效应。在本文中，我们介绍了一种新颖的因果推论方法，该方法利用了多种结果的信息来处理未衡量的混杂。我们方法中的关键假设是多种结果之间的有条件独立性。与文献中现有的建议相反，在我们的关键识别假设中，多个结果的作用是对称的，因此是平行结果的名称。我们以至少三个并行结果显示非参数可识别性，并在一组线性结构方程模型下提供参数估计工具。通过一组合成和真实数据分析来评估我们的建议。
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.
