Assignment-Control Plots: A Visual Companion for Causal Inference Study Design. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Aikens, Rachael C.; Baiocchi, Michael
• Is Part Of: American statistician. Volume 77:Issue 1(2023)
• Page Start: 72
• Page End: 84
• Publication Date: 2023-01-02
• Subjects: Instrumental variable -- Matching -- Observational study -- Prognostic score -- Propensity score -- Visualization; Statistics -- Periodicals; 001.42205
• DOI: 10.1080/00031305.2022.2051605
• Languages: English
• ISSNs: 0003-1305

Abstract

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.