JOINTVIP: Prioritizing variables in observational study design with joint variable importance plot in R

TL;DR

This paper introduces the jointVIP package, which helps researchers prioritize variables for adjustment in observational studies by visualizing their importance to treatment and outcome, improving causal effect estimation.

Contribution

The paper presents the jointVIP tool that quantifies and visualizes variable importance for better variable prioritization in observational study design.

Findings

jointVIP effectively guides variable prioritization

Improves balance between treated and control groups

Facilitates better causal inference in observational studies

Abstract

Credible causal effect estimation requires treated subjects and controls to be otherwise similar. In observational settings, such as analysis of electronic health records, this is not guaranteed. Investigators must balance background variables so they are similar in treated and control groups. Common approaches include matching (grouping individuals into small homogeneous sets) or weighting (upweighting or downweighting individuals) to create similar profiles. However, creating identical distributions may be impossible if many variables are measured, and not all variables are of equal importance to the outcome. The joint variable importance plot (jointVIP) package to guides decisions about which variables to prioritize for adjustment by quantifying and visualizing each variable's relationship to both treatment and outcome.