Assignment-Control Plots: A Visual Companion for Causal Inference Study Design

TL;DR

Assignment-control plots are visual tools that help researchers understand and balance baseline covariates in causal inference studies, improving study design and interpretation.

Contribution

This paper introduces assignment-control plots, a novel visualization method for decomposing covariate variation relevant to causal study design.

Findings

Visualize covariate distributions in observational and experimental studies.

Illustrate core causal inference concepts through practical examples.

Suggest new directions for methodological research in study design.

Abstract

An important step for any causal inference study design is understanding the distribution of the treated and control subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. In the observational context, balancing on baseline variation summarized in a propensity score can help reduce bias due to self-selection. In both observational and experimental studies, controlling baseline variation associated with the expected outcomes can help increase the precision of causal effect estimates. We propose a set of visualizations which decompose the space of measured covariates into the different types of baseline variation important to the study design. These ``assignment-control plots'' and variations thereof visually illustrate core concepts of causal inference and suggest new directions for methodological research on study design. As a practical demonstration, we illustrate one application of assignment-control plots to a study of cardiothoracic surgery. 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.