Abstract

An important step for any causal inference study design is understanding the distribution of the subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. We propose a set of visualizations that reduce the space of measured covariates into two components of baseline variation important to the design of an observational causal inference study: a propensity score summarizing baseline variation associated with treatment assignment and a prognostic score summarizing baseline variation associated with the untreated potential outcome. These assignment-control plots and variations thereof visualize study design tradeoffs and illustrate core methodological concepts in causal inference. As a practical demonstration, we apply assignment-control plots to a hypothetical study of cardiothoracic surgery. To demonstrate how these plots can be used to illustrate nuanced concepts, we use them to visualize unmeasured confounding and to consider the relationship between propensity scores and instrumental variables. While the family of visualization tools for studies of causality is relatively sparse, simple visual tools can be an asset to education, application, and methods development.

 抽象的

任何因果推理研究设计的一个重要步骤是了解受试者在测量基线协变量方面的分布。然而，并非所有基线变化都同样重要。我们提出了一组可视化，将测量协变量的空间减少为对观察性因果推理研究的设计很重要的基线变化的两个组成部分：总结与治疗分配相关的基线变化的倾向得分和总结与治疗分配相关的基线变化的预后得分未经治疗的潜在结果。这些分配控制图及其变体可视化研究设计权衡，并说明因果推理中的核心方法概念。作为实际演示，我们将分配控制图应用于心胸外科的假设研究。为了演示如何使用这些图来说明微妙的概念，我们使用它们来可视化未测量的混杂因素，并考虑倾向得分和工具变量之间的关系。虽然用于因果关系研究的可视化工具系列相对较少，但简单的可视化工具可以成为教育、应用和方法开发的财富。