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.

翻译：可信的因果效应估计要求处理组和对照组在其他方面具有相似性。在观察性研究中（如电子健康记录分析），这种相似性无法保证。研究者必须平衡背景变量，使处理组和对照组具有可比性。常用方法包括匹配（将个体分为同质的小组）或加权（提高或降低个体权重）以创建相似的变量分布。然而，当测量变量众多且不同变量对结局的重要性存在差异时，实现完全相同的分布可能不可行。jointVIP（联合变量重要性图）包通过量化并可视化每个变量与处理及结局的关系，为优先调整哪些变量提供决策指导。