论文标题
美元
$ρ$-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows
论文作者
论文摘要
我们提出了一种新颖的灵敏度分析，以在观察性研究中使用Copulas并标准化流动。使用结构性因果模型的介入等效性的想法，我们开发了$ρ$ -gnf（$ρ$ - 晶量标准化流），其中$ρ{\ in} [ - 1，+1] $是一个有界的灵敏度参数。该参数代表由于未观察到的混杂而导致的后门非因果关系，并且用高斯副群编码。换句话说，$ρ$ -GNF使学者能够将平均因果效应（ACE）估计为$ρ$的函数，同时考虑了未观察到的混杂的各种假定优势。 $ρ$ -gnf的输出是我们将其表示为$ρ_{curve} $，它为ACE提供了一个假定的$ρ$值的ACE的界限。特别是，$ρ_{curve} $使学者能够确定与其他敏感性分析方法（例如，电子价值）相似的混淆强度。利用模拟和现实世界数据的实验，我们显示了$ρ$ -GNF的好处。一个好处是，$ρ$ -GNF使用高斯副总统编码未观察到的原因的分布，这是在许多应用设置中通常使用的。与其他流行的灵敏度分析方法相比，这种分布假设会产生较窄的ACE界限。
We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop $ρ$-GNF ($ρ$-graphical normalizing flow), where $ρ{\in}[-1,+1]$ is a bounded sensitivity parameter. This parameter represents the back-door non-causal association due to unobserved confounding, and which is encoded with a Gaussian copula. In other words, the $ρ$-GNF enables scholars to estimate the average causal effect (ACE) as a function of $ρ$, while accounting for various assumed strengths of the unobserved confounding. The output of the $ρ$-GNF is what we denote as the $ρ_{curve}$ that provides the bounds for the ACE given an interval of assumed $ρ$ values. In particular, the $ρ_{curve}$ enables scholars to identify the confounding strength required to nullify the ACE, similar to other sensitivity analysis methods (e.g., the E-value). Leveraging on experiments from simulated and real-world data, we show the benefits of $ρ$-GNF. One benefit is that the $ρ$-GNF uses a Gaussian copula to encode the distribution of the unobserved causes, which is commonly used in many applied settings. This distributional assumption produces narrower ACE bounds compared to other popular sensitivity analysis methods.
